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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 17 Nov 2013 04:52:38 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/17/t1384681977a9pdh8i3k8pi250.htm/, Retrieved Mon, 29 Apr 2024 00:16:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225693, Retrieved Mon, 29 Apr 2024 00:16:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
- R P     [Multiple Regression] [Workshop 7 ] [2013-11-17 09:52:38] [9964622deab74e5b362b727a820903f4] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53
39	32	16	11	18	11	83
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	9	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	9.5	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	12	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	31	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	10	7	16	8	71
29	31	10	9	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	15	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	35	14	6	15	14.5	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	80
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	12.5	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	85
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	67
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
36	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	85
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	11	9	13	12	69
32	34	16	8	15	10	54
30	37	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
38	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	38	12	8	12	18	84
34	36	13	8	13	16	69
27	26	13	10	10	24	66
31	26	12	8	14	14	81
38	33	17	14	16	20	82
34	39	15	10	10	18	72
24	30	10	8	11	23	54
30	33	14	11	14	12	78
26	25	11	12	12	14	74
34	38	13	12	9	16	82
27	37	16	12	9	18	73
37	31	12	5	11	20	55
36	37	16	12	16	12	72
41	35	12	10	9	12	78
29	25	9	7	13	17	59
36	28	12	12	16	13	72
32	35	15	11	13	9	78
37	33	12	8	9	16	68
30	30	12	9	12	18	69
31	31	14	10	16	10	67
38	37	12	9	11	14	74
36	36	16	12	14	11	54
35	30	11	6	13	9	67
31	36	19	15	15	11	70
38	32	15	12	14	10	80
22	28	8	12	16	11	89
32	36	16	12	13	19	76
36	34	17	11	14	14	74
39	31	12	7	15	12	87
28	28	11	7	13	14	54
32	36	11	5	11	21	61
32	36	14	12	11	13	38
38	40	16	12	14	10	75
32	33	12	3	15	15	69
35	37	16	11	11	16	62
32	32	13	10	15	14	72
37	38	15	12	12	12	70
34	31	16	9	14	19	79
33	37	16	12	14	15	87
33	33	14	9	8	19	62
26	32	16	12	13	13	77
30	30	16	12	9	17	69
24	30	14	10	15	12	69
34	31	11	9	17	11	75
34	32	12	12	13	14	54
33	34	15	8	15	11	72
34	36	15	11	15	13	74
35	37	16	11	14	12	85
35	36	16	12	16	15	52
36	33	11	10	13	14	70
34	33	15	10	16	12	84
34	33	12	12	9	17	64
41	44	12	12	16	11	84
32	39	15	11	11	18	87
30	32	15	8	10	13	79
35	35	16	12	11	17	67
28	25	14	10	15	13	65
33	35	17	11	17	11	85
39	34	14	10	14	12	83
36	35	13	8	8	22	61
36	39	15	12	15	14	82
35	33	13	12	11	12	76
38	36	14	10	16	12	58
33	32	15	12	10	17	72
31	32	12	9	15	9	72
34	36	13	9	9	21	38
32	36	8	6	16	10	78
31	32	14	10	19	11	54
33	34	14	9	12	12	63
34	33	11	9	8	23	66
34	35	12	9	11	13	70
34	30	13	6	14	12	71
33	38	10	10	9	16	67
32	34	16	6	15	9	58
41	33	18	14	13	17	72
34	32	13	10	16	9	72
36	31	11	10	11	14	70
37	30	4	6	12	17	76
36	27	13	12	13	13	50
29	31	16	12	10	11	72
37	30	10	7	11	12	72
27	32	12	8	12	10	88
35	35	12	11	8	19	53
28	28	10	3	12	16	58
35	33	13	6	12	16	66
37	31	15	10	15	14	82
29	35	12	8	11	20	69
32	35	14	9	13	15	68
36	32	10	9	14	23	44
19	21	12	8	10	20	56
21	20	12	9	12	16	53
31	34	11	7	15	14	70
33	32	10	7	13	17	78
36	34	12	6	13	11	71
33	32	16	9	13	13	72
37	33	12	10	12	17	68
34	33	14	11	12	15	67
35	37	16	12	9	21	75
31	32	14	8	9	18	62
37	34	13	11	15	15	67
35	30	4	3	10	8	83
27	30	15	11	14	12	64
34	38	11	12	15	12	68
40	36	11	7	7	22	62
29	32	14	9	14	12	72

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 13 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.8448319096304 + 0.00296943588586354Connected[t] + 0.0120368104937264Separate[t] + 0.0816224658026396Learning[t] -0.0341130700519477Software[t] -0.360391988863244Depression[t] + 0.0261256527885332Belonging[t] -0.00437230566099058t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  15.8448319096304 +  0.00296943588586354Connected[t] +  0.0120368104937264Separate[t] +  0.0816224658026396Learning[t] -0.0341130700519477Software[t] -0.360391988863244Depression[t] +  0.0261256527885332Belonging[t] -0.00437230566099058t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  15.8448319096304 +  0.00296943588586354Connected[t] +  0.0120368104937264Separate[t] +  0.0816224658026396Learning[t] -0.0341130700519477Software[t] -0.360391988863244Depression[t] +  0.0261256527885332Belonging[t] -0.00437230566099058t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.8448319096304 + 0.00296943588586354Connected[t] + 0.0120368104937264Separate[t] + 0.0816224658026396Learning[t] -0.0341130700519477Software[t] -0.360391988863244Depression[t] + 0.0261256527885332Belonging[t] -0.00437230566099058t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.84483190963041.8816818.420600
Connected0.002969435885863540.0371730.07990.9363950.468197
Separate0.01203681049372640.0379160.31750.7511510.375575
Learning0.08162246580263960.0671841.21490.2255180.112759
Software-0.03411307005194770.069036-0.49410.6216340.310817
Depression-0.3603919888632440.039034-9.232800
Belonging0.02612565278853320.01272.05710.0406910.020345
t-0.004372305660990580.001821-2.40130.0170510.008526

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 15.8448319096304 & 1.881681 & 8.4206 & 0 & 0 \tabularnewline
Connected & 0.00296943588586354 & 0.037173 & 0.0799 & 0.936395 & 0.468197 \tabularnewline
Separate & 0.0120368104937264 & 0.037916 & 0.3175 & 0.751151 & 0.375575 \tabularnewline
Learning & 0.0816224658026396 & 0.067184 & 1.2149 & 0.225518 & 0.112759 \tabularnewline
Software & -0.0341130700519477 & 0.069036 & -0.4941 & 0.621634 & 0.310817 \tabularnewline
Depression & -0.360391988863244 & 0.039034 & -9.2328 & 0 & 0 \tabularnewline
Belonging & 0.0261256527885332 & 0.0127 & 2.0571 & 0.040691 & 0.020345 \tabularnewline
t & -0.00437230566099058 & 0.001821 & -2.4013 & 0.017051 & 0.008526 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]15.8448319096304[/C][C]1.881681[/C][C]8.4206[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]0.00296943588586354[/C][C]0.037173[/C][C]0.0799[/C][C]0.936395[/C][C]0.468197[/C][/ROW]
[ROW][C]Separate[/C][C]0.0120368104937264[/C][C]0.037916[/C][C]0.3175[/C][C]0.751151[/C][C]0.375575[/C][/ROW]
[ROW][C]Learning[/C][C]0.0816224658026396[/C][C]0.067184[/C][C]1.2149[/C][C]0.225518[/C][C]0.112759[/C][/ROW]
[ROW][C]Software[/C][C]-0.0341130700519477[/C][C]0.069036[/C][C]-0.4941[/C][C]0.621634[/C][C]0.310817[/C][/ROW]
[ROW][C]Depression[/C][C]-0.360391988863244[/C][C]0.039034[/C][C]-9.2328[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0261256527885332[/C][C]0.0127[/C][C]2.0571[/C][C]0.040691[/C][C]0.020345[/C][/ROW]
[ROW][C]t[/C][C]-0.00437230566099058[/C][C]0.001821[/C][C]-2.4013[/C][C]0.017051[/C][C]0.008526[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.84483190963041.8816818.420600
Connected0.002969435885863540.0371730.07990.9363950.468197
Separate0.01203681049372640.0379160.31750.7511510.375575
Learning0.08162246580263960.0671841.21490.2255180.112759
Software-0.03411307005194770.069036-0.49410.6216340.310817
Depression-0.3603919888632440.039034-9.232800
Belonging0.02612565278853320.01272.05710.0406910.020345
t-0.004372305660990580.001821-2.40130.0170510.008526






Multiple Linear Regression - Regression Statistics
Multiple R0.614034238205654
R-squared0.377038045688798
Adjusted R-squared0.360003929750601
F-TEST (value)22.134289038349
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.99890818991702
Sum Squared Residuals1022.88229163964

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.614034238205654 \tabularnewline
R-squared & 0.377038045688798 \tabularnewline
Adjusted R-squared & 0.360003929750601 \tabularnewline
F-TEST (value) & 22.134289038349 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.99890818991702 \tabularnewline
Sum Squared Residuals & 1022.88229163964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.614034238205654[/C][/ROW]
[ROW][C]R-squared[/C][C]0.377038045688798[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.360003929750601[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.134289038349[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.99890818991702[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1022.88229163964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.614034238205654
R-squared0.377038045688798
Adjusted R-squared0.360003929750601
F-TEST (value)22.134289038349
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.99890818991702
Sum Squared Residuals1022.88229163964






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.1312962202957-0.131296220295746
21815.47190621987972.52809378012027
31114.0600224759325-3.06002247593248
41214.7619833829419-2.76198338294189
51611.48585564607474.51414435392527
61814.70465424204933.29534575795074
71410.90068074728533.09931925271466
81415.2166338141353-1.2166338141353
91515.3964053161856-0.396405316185608
101514.58614989283750.413850107162524
111715.70755167031231.29244832968771
121915.732767442273.26723255772999
131013.5632603830827-3.5632603830827
141613.6528395174532.34716048254699
151815.86749846905252.13250153094747
161413.55189187776090.448108122239102
171414.0597227426586-0.0597227426585944
181715.89420581348171.10579418651834
191415.4970474040916-1.49704740409165
201613.93825464650062.06174535349944
211815.47653505750342.5234649424966
221113.8414939806096-2.84149398060961
231414.5109140015346-0.510914001534607
241213.6674657051422-1.66746570514217
251715.49722954379321.50277045620676
26916.0287131008473-7.02871310084732
271615.24103420271080.758965797289226
281413.40930493939650.590695060603495
291514.10215686162720.897843138372813
301114.0787450085401-3.07874500854006
311615.83598854163670.164011458363326
321312.88174128004450.118258719955501
331715.23614135597111.76385864402891
341515.3693418945039-0.369341894503936
351414.1354264781698-0.13542647816976
361615.7288842052180.271115794781976
37911.0895947421409-2.0895947421409
381514.44637389493730.55362610506266
391715.48002523046621.51997476953382
401315.3346635915881-2.33466359158809
411515.815213600011-0.815213600011012
421613.80666443394932.19333556605071
431615.80324187061830.196758129381705
441213.2682253844808-1.26822538448081
451514.73788939800360.262110601996426
461113.754802161886-2.75480216188597
471515.3754918585291-0.375491858529096
481514.98640947703550.0135905229644654
491713.65302157856823.34697842143183
501314.8527028883008-1.85270288830083
511615.2797686522860.720231347713954
521413.58011299188210.419887008117888
531111.834309479386-0.834309479386016
541213.6038791937884-1.60387919378844
551214.3301960725687-2.33019607256873
561513.82904216200911.17095783799093
571614.26547510663751.73452489336253
581515.4900968280511-0.490096828051089
591215.2824061625817-3.28240616258172
601213.4834199973343-1.48341999733432
61810.9217329545057-2.92173295450565
621314.6794130896755-1.6794130896755
631114.6853506692265-3.68535066922648
641413.1588620786530.841137921346952
651513.65702599466841.34297400533162
661015.1754043338347-5.17540433383466
671112.6693261666038-1.66932616660384
681214.6380214856697-2.63802148566966
691513.67135625778351.32864374221648
701513.74184756817641.25815243182359
711413.68920492835740.310795071642587
721612.85185338156013.14814661843988
731514.51875345012210.481246549877948
741515.3180006421193-0.318000642119303
751315.0697092574661-2.06970925746606
761212.312617716614-0.31261771661396
771714.0695848719212.93041512807897
781312.58461592750250.415384072497524
791513.93401971682771.06598028317232
801314.9937704308892-1.99377043088919
811515.0182090181151-0.0182090181151229
821515.5435911501378-0.543591150137809
831614.38646334745831.61353665254167
841514.38726809012520.612731909874834
851414.2357265998029-0.235726599802869
861514.16111279039060.83888720960939
871414.3462530464131-0.34625304641315
881312.83286861780350.167131382196479
89710.6660549229509-3.66605492295089
901713.87123084788213.12876915211792
911312.81982100766840.180178992331609
921514.2632012967810.73679870321897
931413.42166396745750.578336032542457
941314.1179407101022-1.11794071010218
951615.06916514706890.930834852931136
961212.8551358239965-0.85513582399649
971414.9702205042097-0.970220504209678
981714.98393525001132.01606474998865
991515.1235343193277-0.12353431932769
1001715.16377128037941.83622871962063
1011213.0060030469496-1.00600304694957
1021614.96784829830831.03215170169169
1031114.435611491736-3.43561149173603
1041513.18590954512921.81409045487076
105911.4705270024897-2.47052700248975
1061614.9149572529631.08504274703701
1071512.9534298228732.04657017712703
1081012.8364320573179-2.83643205731788
109109.294532771233790.705467228766212
1101513.93400817544281.06599182455718
1111113.204568185533-2.20456818553296
1121315.2907485701586-2.29074857015857
1131412.11574132245761.88425867754243
1141814.16469438352293.83530561647707
1151615.50568454714630.4943154528537
1161412.98165027399511.01834972600493
1171413.85083822959390.149161770406144
1181415.0931700206499-1.09317002064993
1191413.71272280764770.287277192352314
1201212.5542623053079-0.554262305307939
1211413.5353806304530.464619369547046
1221514.90629122740240.0937087725975806
1231515.8050964130047-0.805096413004664
1241514.50643467729570.4935653227043
1251314.6814390556228-1.68143905562279
1261716.14628645827570.853713541724324
1271715.23135644471041.7686435552896
1281914.88567870197884.11432129802116
1291513.58212644627331.41787355372671
1301314.6241070911552-1.62410709115517
131910.6978162028774-1.69781620287735
1321515.2726372545933-0.272637254593344
1331512.6011077578152.39889224218498
1341514.16816299820230.83183700179766
1351613.68883288741852.31116711258149
136119.473996307094811.52600369290518
1371413.21759671953480.782403280465168
1381111.9530550243485-0.953055024348489
1391514.16455816573120.835441834268822
1401313.8148456662453-0.814845666245294
1411514.57253057094010.427469429059923
1421613.8247959173372.17520408266303
1431414.5279517852857-0.527951785285674
1441514.20180612889420.798193871105817
1451614.60695746782141.39304253217858
1461614.31307133414581.68692866585418
1471113.2765063740308-2.2765063740308
1481214.5576084393201-2.55760843932013
149911.4962658480754-2.49626584807536
1501614.24467002172911.75532997827087
1511312.47282188627820.527178113721773
1521615.34893633863220.651063661367826
1531214.4068591757054-2.40685917570542
154911.5186191731159-2.5186191731159
1551311.73232225729391.2676777427061
1561312.5356211397040.464378860295996
1571413.31235969091640.687640309083596
1581914.75450953214914.24549046785088
1591315.5175166821899-2.51751668218992
1601212.1117478148956-0.111747814895645
1611312.49976241172010.500237588279941
162109.324496940545310.675503059454692
1631413.31441073318950.68558926681053
1641611.48228978049624.51771021950381
1651011.9709953926978-1.97099539269777
166119.216489550315481.78351044968452
1671414.0815224889261-0.0815224889260541
1681212.8647108994313-0.864710899431311
169912.6920387934627-3.69203879346273
170911.9437961706916-2.9437961706916
1711110.61815326015990.381846739840127
1721614.09800276273331.90199723726668
173913.9828942091387-4.98289420913873
1741311.38164503335971.61835496664031
1751613.2896726992332.71032730076699
1761315.2349826631287-2.23498266312869
177912.2948552787294-3.29485527872945
1781211.50481509539630.49518490460367
1791614.47546550299711.52453449700285
1801113.176279864013-2.17627986401299
1811413.93674543996030.063254560039674
1821314.714166390727-1.71416639072702
1831514.47369228107760.526307718922366
1841414.8394566483366-0.839456648336643
1851614.0428077521421.95719224785799
1861311.59463461855371.40536538144631
1871413.44351061004240.556489389957552
1881514.20069359572990.799306404270118
1891312.46299407829250.537005921707511
1901110.29515578770570.704844212294319
1911112.5791052858587-1.57910528585867
1921414.8517668888585-0.851766888858504
1931512.76713420063582.2328657993642
1941112.3301311890193-1.3301311890193
1951513.02795270148791.97204729851206
1961213.8741999018694-1.87419990186943
1971411.67301024410732.32698975589266
1981413.28612333312820.71387666687176
199811.0779887888766-3.0779887888766
2001313.6559360679777-0.655936067977739
201911.9887947071115-2.98879470711151
2021513.67354693895021.32645306104983
2031714.017297380882.98270261911998
2041312.37443046621060.625569533789382
2051514.32391974005030.676080259949742
2061513.57571860895731.42428139104268
2071414.3157491850157-0.315749185015666
2081612.32190449019773.67809550980233
2091312.77515873908890.224841260911094
2101614.17788054163271.8221194583673
211911.535941698373-2.53594169837301
2121614.36962534829421.63037465170582
2131112.1129575709745-1.11295757097455
2141013.7136826522495-3.71368265224954
2151111.9503623541785-0.950362354178522
2161513.09913375075381.90086624924625
2171714.68402809031252.31597190968754
2181414.0620379676766-0.0620379676766466
21989.86871358917287-1.86871358917287
2201513.37105579634941.62894420365057
2211113.6922783212302-2.69227832123023
2221613.41251161042092.58748838957906
2231011.9223404037777-1.92234040377772
2241514.65263694999890.347363050001121
22599.57396659860397-0.573966598603964
2261614.26722029135091.73277970864909
2271913.57760616664925.42239383335085
2281213.5120983100328-1.51209831003284
22989.36785631322598-1.36785631322598
2301113.1776025941417-2.17760259414166
2311413.68352555362230.316474446377701
232911.8450880518024-2.84508805180244
2331514.70339919025020.296600809749783
2341312.08667859639150.913321403608521
2351614.66095929113631.33904070886374
2361112.6330328652547-1.63303286525471
2371211.26026615471660.739733845283366
2381312.50903873655180.490961263448215
2391014.0724433581468-4.07244335814679
2401113.4002282956596-2.40022829565964
2411214.6581615360238-2.65816153602382
242810.4533901914072-2.45339019140721
2431211.66543802043180.334561979568206
2441212.0935692280008-0.0935692280008212
2451513.23664924686461.76335075313539
2461110.57804201935720.421957980642801
2471312.48754417443480.512455825565181
248148.622297739794795.37770226020521
2491010.0270819103525-0.02708191035249
2501211.34568959300490.65431040699507
2511512.69105074254762.30894925745245
2521311.71475047758671.28524952241327
2531313.9201904658877-0.920190465887744
2541313.4123285596985-0.412328559698469
2551211.5251973082050.474802691794957
2561212.3357068813777-0.335706881377749
257910.5582364042597-1.55823640425966
258911.1965521315279-2.1965521315279
2591512.26191261674352.73808738325654
2601014.6825109321869-4.6825109321869
2611413.34137034441730.658629655582655
2621513.19797825179881.80202174820116
26379.59724048536167-2.59724048536167
2641413.55387481680310.446125183196925

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.1312962202957 & -0.131296220295746 \tabularnewline
2 & 18 & 15.4719062198797 & 2.52809378012027 \tabularnewline
3 & 11 & 14.0600224759325 & -3.06002247593248 \tabularnewline
4 & 12 & 14.7619833829419 & -2.76198338294189 \tabularnewline
5 & 16 & 11.4858556460747 & 4.51414435392527 \tabularnewline
6 & 18 & 14.7046542420493 & 3.29534575795074 \tabularnewline
7 & 14 & 10.9006807472853 & 3.09931925271466 \tabularnewline
8 & 14 & 15.2166338141353 & -1.2166338141353 \tabularnewline
9 & 15 & 15.3964053161856 & -0.396405316185608 \tabularnewline
10 & 15 & 14.5861498928375 & 0.413850107162524 \tabularnewline
11 & 17 & 15.7075516703123 & 1.29244832968771 \tabularnewline
12 & 19 & 15.73276744227 & 3.26723255772999 \tabularnewline
13 & 10 & 13.5632603830827 & -3.5632603830827 \tabularnewline
14 & 16 & 13.652839517453 & 2.34716048254699 \tabularnewline
15 & 18 & 15.8674984690525 & 2.13250153094747 \tabularnewline
16 & 14 & 13.5518918777609 & 0.448108122239102 \tabularnewline
17 & 14 & 14.0597227426586 & -0.0597227426585944 \tabularnewline
18 & 17 & 15.8942058134817 & 1.10579418651834 \tabularnewline
19 & 14 & 15.4970474040916 & -1.49704740409165 \tabularnewline
20 & 16 & 13.9382546465006 & 2.06174535349944 \tabularnewline
21 & 18 & 15.4765350575034 & 2.5234649424966 \tabularnewline
22 & 11 & 13.8414939806096 & -2.84149398060961 \tabularnewline
23 & 14 & 14.5109140015346 & -0.510914001534607 \tabularnewline
24 & 12 & 13.6674657051422 & -1.66746570514217 \tabularnewline
25 & 17 & 15.4972295437932 & 1.50277045620676 \tabularnewline
26 & 9 & 16.0287131008473 & -7.02871310084732 \tabularnewline
27 & 16 & 15.2410342027108 & 0.758965797289226 \tabularnewline
28 & 14 & 13.4093049393965 & 0.590695060603495 \tabularnewline
29 & 15 & 14.1021568616272 & 0.897843138372813 \tabularnewline
30 & 11 & 14.0787450085401 & -3.07874500854006 \tabularnewline
31 & 16 & 15.8359885416367 & 0.164011458363326 \tabularnewline
32 & 13 & 12.8817412800445 & 0.118258719955501 \tabularnewline
33 & 17 & 15.2361413559711 & 1.76385864402891 \tabularnewline
34 & 15 & 15.3693418945039 & -0.369341894503936 \tabularnewline
35 & 14 & 14.1354264781698 & -0.13542647816976 \tabularnewline
36 & 16 & 15.728884205218 & 0.271115794781976 \tabularnewline
37 & 9 & 11.0895947421409 & -2.0895947421409 \tabularnewline
38 & 15 & 14.4463738949373 & 0.55362610506266 \tabularnewline
39 & 17 & 15.4800252304662 & 1.51997476953382 \tabularnewline
40 & 13 & 15.3346635915881 & -2.33466359158809 \tabularnewline
41 & 15 & 15.815213600011 & -0.815213600011012 \tabularnewline
42 & 16 & 13.8066644339493 & 2.19333556605071 \tabularnewline
43 & 16 & 15.8032418706183 & 0.196758129381705 \tabularnewline
44 & 12 & 13.2682253844808 & -1.26822538448081 \tabularnewline
45 & 15 & 14.7378893980036 & 0.262110601996426 \tabularnewline
46 & 11 & 13.754802161886 & -2.75480216188597 \tabularnewline
47 & 15 & 15.3754918585291 & -0.375491858529096 \tabularnewline
48 & 15 & 14.9864094770355 & 0.0135905229644654 \tabularnewline
49 & 17 & 13.6530215785682 & 3.34697842143183 \tabularnewline
50 & 13 & 14.8527028883008 & -1.85270288830083 \tabularnewline
51 & 16 & 15.279768652286 & 0.720231347713954 \tabularnewline
52 & 14 & 13.5801129918821 & 0.419887008117888 \tabularnewline
53 & 11 & 11.834309479386 & -0.834309479386016 \tabularnewline
54 & 12 & 13.6038791937884 & -1.60387919378844 \tabularnewline
55 & 12 & 14.3301960725687 & -2.33019607256873 \tabularnewline
56 & 15 & 13.8290421620091 & 1.17095783799093 \tabularnewline
57 & 16 & 14.2654751066375 & 1.73452489336253 \tabularnewline
58 & 15 & 15.4900968280511 & -0.490096828051089 \tabularnewline
59 & 12 & 15.2824061625817 & -3.28240616258172 \tabularnewline
60 & 12 & 13.4834199973343 & -1.48341999733432 \tabularnewline
61 & 8 & 10.9217329545057 & -2.92173295450565 \tabularnewline
62 & 13 & 14.6794130896755 & -1.6794130896755 \tabularnewline
63 & 11 & 14.6853506692265 & -3.68535066922648 \tabularnewline
64 & 14 & 13.158862078653 & 0.841137921346952 \tabularnewline
65 & 15 & 13.6570259946684 & 1.34297400533162 \tabularnewline
66 & 10 & 15.1754043338347 & -5.17540433383466 \tabularnewline
67 & 11 & 12.6693261666038 & -1.66932616660384 \tabularnewline
68 & 12 & 14.6380214856697 & -2.63802148566966 \tabularnewline
69 & 15 & 13.6713562577835 & 1.32864374221648 \tabularnewline
70 & 15 & 13.7418475681764 & 1.25815243182359 \tabularnewline
71 & 14 & 13.6892049283574 & 0.310795071642587 \tabularnewline
72 & 16 & 12.8518533815601 & 3.14814661843988 \tabularnewline
73 & 15 & 14.5187534501221 & 0.481246549877948 \tabularnewline
74 & 15 & 15.3180006421193 & -0.318000642119303 \tabularnewline
75 & 13 & 15.0697092574661 & -2.06970925746606 \tabularnewline
76 & 12 & 12.312617716614 & -0.31261771661396 \tabularnewline
77 & 17 & 14.069584871921 & 2.93041512807897 \tabularnewline
78 & 13 & 12.5846159275025 & 0.415384072497524 \tabularnewline
79 & 15 & 13.9340197168277 & 1.06598028317232 \tabularnewline
80 & 13 & 14.9937704308892 & -1.99377043088919 \tabularnewline
81 & 15 & 15.0182090181151 & -0.0182090181151229 \tabularnewline
82 & 15 & 15.5435911501378 & -0.543591150137809 \tabularnewline
83 & 16 & 14.3864633474583 & 1.61353665254167 \tabularnewline
84 & 15 & 14.3872680901252 & 0.612731909874834 \tabularnewline
85 & 14 & 14.2357265998029 & -0.235726599802869 \tabularnewline
86 & 15 & 14.1611127903906 & 0.83888720960939 \tabularnewline
87 & 14 & 14.3462530464131 & -0.34625304641315 \tabularnewline
88 & 13 & 12.8328686178035 & 0.167131382196479 \tabularnewline
89 & 7 & 10.6660549229509 & -3.66605492295089 \tabularnewline
90 & 17 & 13.8712308478821 & 3.12876915211792 \tabularnewline
91 & 13 & 12.8198210076684 & 0.180178992331609 \tabularnewline
92 & 15 & 14.263201296781 & 0.73679870321897 \tabularnewline
93 & 14 & 13.4216639674575 & 0.578336032542457 \tabularnewline
94 & 13 & 14.1179407101022 & -1.11794071010218 \tabularnewline
95 & 16 & 15.0691651470689 & 0.930834852931136 \tabularnewline
96 & 12 & 12.8551358239965 & -0.85513582399649 \tabularnewline
97 & 14 & 14.9702205042097 & -0.970220504209678 \tabularnewline
98 & 17 & 14.9839352500113 & 2.01606474998865 \tabularnewline
99 & 15 & 15.1235343193277 & -0.12353431932769 \tabularnewline
100 & 17 & 15.1637712803794 & 1.83622871962063 \tabularnewline
101 & 12 & 13.0060030469496 & -1.00600304694957 \tabularnewline
102 & 16 & 14.9678482983083 & 1.03215170169169 \tabularnewline
103 & 11 & 14.435611491736 & -3.43561149173603 \tabularnewline
104 & 15 & 13.1859095451292 & 1.81409045487076 \tabularnewline
105 & 9 & 11.4705270024897 & -2.47052700248975 \tabularnewline
106 & 16 & 14.914957252963 & 1.08504274703701 \tabularnewline
107 & 15 & 12.953429822873 & 2.04657017712703 \tabularnewline
108 & 10 & 12.8364320573179 & -2.83643205731788 \tabularnewline
109 & 10 & 9.29453277123379 & 0.705467228766212 \tabularnewline
110 & 15 & 13.9340081754428 & 1.06599182455718 \tabularnewline
111 & 11 & 13.204568185533 & -2.20456818553296 \tabularnewline
112 & 13 & 15.2907485701586 & -2.29074857015857 \tabularnewline
113 & 14 & 12.1157413224576 & 1.88425867754243 \tabularnewline
114 & 18 & 14.1646943835229 & 3.83530561647707 \tabularnewline
115 & 16 & 15.5056845471463 & 0.4943154528537 \tabularnewline
116 & 14 & 12.9816502739951 & 1.01834972600493 \tabularnewline
117 & 14 & 13.8508382295939 & 0.149161770406144 \tabularnewline
118 & 14 & 15.0931700206499 & -1.09317002064993 \tabularnewline
119 & 14 & 13.7127228076477 & 0.287277192352314 \tabularnewline
120 & 12 & 12.5542623053079 & -0.554262305307939 \tabularnewline
121 & 14 & 13.535380630453 & 0.464619369547046 \tabularnewline
122 & 15 & 14.9062912274024 & 0.0937087725975806 \tabularnewline
123 & 15 & 15.8050964130047 & -0.805096413004664 \tabularnewline
124 & 15 & 14.5064346772957 & 0.4935653227043 \tabularnewline
125 & 13 & 14.6814390556228 & -1.68143905562279 \tabularnewline
126 & 17 & 16.1462864582757 & 0.853713541724324 \tabularnewline
127 & 17 & 15.2313564447104 & 1.7686435552896 \tabularnewline
128 & 19 & 14.8856787019788 & 4.11432129802116 \tabularnewline
129 & 15 & 13.5821264462733 & 1.41787355372671 \tabularnewline
130 & 13 & 14.6241070911552 & -1.62410709115517 \tabularnewline
131 & 9 & 10.6978162028774 & -1.69781620287735 \tabularnewline
132 & 15 & 15.2726372545933 & -0.272637254593344 \tabularnewline
133 & 15 & 12.601107757815 & 2.39889224218498 \tabularnewline
134 & 15 & 14.1681629982023 & 0.83183700179766 \tabularnewline
135 & 16 & 13.6888328874185 & 2.31116711258149 \tabularnewline
136 & 11 & 9.47399630709481 & 1.52600369290518 \tabularnewline
137 & 14 & 13.2175967195348 & 0.782403280465168 \tabularnewline
138 & 11 & 11.9530550243485 & -0.953055024348489 \tabularnewline
139 & 15 & 14.1645581657312 & 0.835441834268822 \tabularnewline
140 & 13 & 13.8148456662453 & -0.814845666245294 \tabularnewline
141 & 15 & 14.5725305709401 & 0.427469429059923 \tabularnewline
142 & 16 & 13.824795917337 & 2.17520408266303 \tabularnewline
143 & 14 & 14.5279517852857 & -0.527951785285674 \tabularnewline
144 & 15 & 14.2018061288942 & 0.798193871105817 \tabularnewline
145 & 16 & 14.6069574678214 & 1.39304253217858 \tabularnewline
146 & 16 & 14.3130713341458 & 1.68692866585418 \tabularnewline
147 & 11 & 13.2765063740308 & -2.2765063740308 \tabularnewline
148 & 12 & 14.5576084393201 & -2.55760843932013 \tabularnewline
149 & 9 & 11.4962658480754 & -2.49626584807536 \tabularnewline
150 & 16 & 14.2446700217291 & 1.75532997827087 \tabularnewline
151 & 13 & 12.4728218862782 & 0.527178113721773 \tabularnewline
152 & 16 & 15.3489363386322 & 0.651063661367826 \tabularnewline
153 & 12 & 14.4068591757054 & -2.40685917570542 \tabularnewline
154 & 9 & 11.5186191731159 & -2.5186191731159 \tabularnewline
155 & 13 & 11.7323222572939 & 1.2676777427061 \tabularnewline
156 & 13 & 12.535621139704 & 0.464378860295996 \tabularnewline
157 & 14 & 13.3123596909164 & 0.687640309083596 \tabularnewline
158 & 19 & 14.7545095321491 & 4.24549046785088 \tabularnewline
159 & 13 & 15.5175166821899 & -2.51751668218992 \tabularnewline
160 & 12 & 12.1117478148956 & -0.111747814895645 \tabularnewline
161 & 13 & 12.4997624117201 & 0.500237588279941 \tabularnewline
162 & 10 & 9.32449694054531 & 0.675503059454692 \tabularnewline
163 & 14 & 13.3144107331895 & 0.68558926681053 \tabularnewline
164 & 16 & 11.4822897804962 & 4.51771021950381 \tabularnewline
165 & 10 & 11.9709953926978 & -1.97099539269777 \tabularnewline
166 & 11 & 9.21648955031548 & 1.78351044968452 \tabularnewline
167 & 14 & 14.0815224889261 & -0.0815224889260541 \tabularnewline
168 & 12 & 12.8647108994313 & -0.864710899431311 \tabularnewline
169 & 9 & 12.6920387934627 & -3.69203879346273 \tabularnewline
170 & 9 & 11.9437961706916 & -2.9437961706916 \tabularnewline
171 & 11 & 10.6181532601599 & 0.381846739840127 \tabularnewline
172 & 16 & 14.0980027627333 & 1.90199723726668 \tabularnewline
173 & 9 & 13.9828942091387 & -4.98289420913873 \tabularnewline
174 & 13 & 11.3816450333597 & 1.61835496664031 \tabularnewline
175 & 16 & 13.289672699233 & 2.71032730076699 \tabularnewline
176 & 13 & 15.2349826631287 & -2.23498266312869 \tabularnewline
177 & 9 & 12.2948552787294 & -3.29485527872945 \tabularnewline
178 & 12 & 11.5048150953963 & 0.49518490460367 \tabularnewline
179 & 16 & 14.4754655029971 & 1.52453449700285 \tabularnewline
180 & 11 & 13.176279864013 & -2.17627986401299 \tabularnewline
181 & 14 & 13.9367454399603 & 0.063254560039674 \tabularnewline
182 & 13 & 14.714166390727 & -1.71416639072702 \tabularnewline
183 & 15 & 14.4736922810776 & 0.526307718922366 \tabularnewline
184 & 14 & 14.8394566483366 & -0.839456648336643 \tabularnewline
185 & 16 & 14.042807752142 & 1.95719224785799 \tabularnewline
186 & 13 & 11.5946346185537 & 1.40536538144631 \tabularnewline
187 & 14 & 13.4435106100424 & 0.556489389957552 \tabularnewline
188 & 15 & 14.2006935957299 & 0.799306404270118 \tabularnewline
189 & 13 & 12.4629940782925 & 0.537005921707511 \tabularnewline
190 & 11 & 10.2951557877057 & 0.704844212294319 \tabularnewline
191 & 11 & 12.5791052858587 & -1.57910528585867 \tabularnewline
192 & 14 & 14.8517668888585 & -0.851766888858504 \tabularnewline
193 & 15 & 12.7671342006358 & 2.2328657993642 \tabularnewline
194 & 11 & 12.3301311890193 & -1.3301311890193 \tabularnewline
195 & 15 & 13.0279527014879 & 1.97204729851206 \tabularnewline
196 & 12 & 13.8741999018694 & -1.87419990186943 \tabularnewline
197 & 14 & 11.6730102441073 & 2.32698975589266 \tabularnewline
198 & 14 & 13.2861233331282 & 0.71387666687176 \tabularnewline
199 & 8 & 11.0779887888766 & -3.0779887888766 \tabularnewline
200 & 13 & 13.6559360679777 & -0.655936067977739 \tabularnewline
201 & 9 & 11.9887947071115 & -2.98879470711151 \tabularnewline
202 & 15 & 13.6735469389502 & 1.32645306104983 \tabularnewline
203 & 17 & 14.01729738088 & 2.98270261911998 \tabularnewline
204 & 13 & 12.3744304662106 & 0.625569533789382 \tabularnewline
205 & 15 & 14.3239197400503 & 0.676080259949742 \tabularnewline
206 & 15 & 13.5757186089573 & 1.42428139104268 \tabularnewline
207 & 14 & 14.3157491850157 & -0.315749185015666 \tabularnewline
208 & 16 & 12.3219044901977 & 3.67809550980233 \tabularnewline
209 & 13 & 12.7751587390889 & 0.224841260911094 \tabularnewline
210 & 16 & 14.1778805416327 & 1.8221194583673 \tabularnewline
211 & 9 & 11.535941698373 & -2.53594169837301 \tabularnewline
212 & 16 & 14.3696253482942 & 1.63037465170582 \tabularnewline
213 & 11 & 12.1129575709745 & -1.11295757097455 \tabularnewline
214 & 10 & 13.7136826522495 & -3.71368265224954 \tabularnewline
215 & 11 & 11.9503623541785 & -0.950362354178522 \tabularnewline
216 & 15 & 13.0991337507538 & 1.90086624924625 \tabularnewline
217 & 17 & 14.6840280903125 & 2.31597190968754 \tabularnewline
218 & 14 & 14.0620379676766 & -0.0620379676766466 \tabularnewline
219 & 8 & 9.86871358917287 & -1.86871358917287 \tabularnewline
220 & 15 & 13.3710557963494 & 1.62894420365057 \tabularnewline
221 & 11 & 13.6922783212302 & -2.69227832123023 \tabularnewline
222 & 16 & 13.4125116104209 & 2.58748838957906 \tabularnewline
223 & 10 & 11.9223404037777 & -1.92234040377772 \tabularnewline
224 & 15 & 14.6526369499989 & 0.347363050001121 \tabularnewline
225 & 9 & 9.57396659860397 & -0.573966598603964 \tabularnewline
226 & 16 & 14.2672202913509 & 1.73277970864909 \tabularnewline
227 & 19 & 13.5776061666492 & 5.42239383335085 \tabularnewline
228 & 12 & 13.5120983100328 & -1.51209831003284 \tabularnewline
229 & 8 & 9.36785631322598 & -1.36785631322598 \tabularnewline
230 & 11 & 13.1776025941417 & -2.17760259414166 \tabularnewline
231 & 14 & 13.6835255536223 & 0.316474446377701 \tabularnewline
232 & 9 & 11.8450880518024 & -2.84508805180244 \tabularnewline
233 & 15 & 14.7033991902502 & 0.296600809749783 \tabularnewline
234 & 13 & 12.0866785963915 & 0.913321403608521 \tabularnewline
235 & 16 & 14.6609592911363 & 1.33904070886374 \tabularnewline
236 & 11 & 12.6330328652547 & -1.63303286525471 \tabularnewline
237 & 12 & 11.2602661547166 & 0.739733845283366 \tabularnewline
238 & 13 & 12.5090387365518 & 0.490961263448215 \tabularnewline
239 & 10 & 14.0724433581468 & -4.07244335814679 \tabularnewline
240 & 11 & 13.4002282956596 & -2.40022829565964 \tabularnewline
241 & 12 & 14.6581615360238 & -2.65816153602382 \tabularnewline
242 & 8 & 10.4533901914072 & -2.45339019140721 \tabularnewline
243 & 12 & 11.6654380204318 & 0.334561979568206 \tabularnewline
244 & 12 & 12.0935692280008 & -0.0935692280008212 \tabularnewline
245 & 15 & 13.2366492468646 & 1.76335075313539 \tabularnewline
246 & 11 & 10.5780420193572 & 0.421957980642801 \tabularnewline
247 & 13 & 12.4875441744348 & 0.512455825565181 \tabularnewline
248 & 14 & 8.62229773979479 & 5.37770226020521 \tabularnewline
249 & 10 & 10.0270819103525 & -0.02708191035249 \tabularnewline
250 & 12 & 11.3456895930049 & 0.65431040699507 \tabularnewline
251 & 15 & 12.6910507425476 & 2.30894925745245 \tabularnewline
252 & 13 & 11.7147504775867 & 1.28524952241327 \tabularnewline
253 & 13 & 13.9201904658877 & -0.920190465887744 \tabularnewline
254 & 13 & 13.4123285596985 & -0.412328559698469 \tabularnewline
255 & 12 & 11.525197308205 & 0.474802691794957 \tabularnewline
256 & 12 & 12.3357068813777 & -0.335706881377749 \tabularnewline
257 & 9 & 10.5582364042597 & -1.55823640425966 \tabularnewline
258 & 9 & 11.1965521315279 & -2.1965521315279 \tabularnewline
259 & 15 & 12.2619126167435 & 2.73808738325654 \tabularnewline
260 & 10 & 14.6825109321869 & -4.6825109321869 \tabularnewline
261 & 14 & 13.3413703444173 & 0.658629655582655 \tabularnewline
262 & 15 & 13.1979782517988 & 1.80202174820116 \tabularnewline
263 & 7 & 9.59724048536167 & -2.59724048536167 \tabularnewline
264 & 14 & 13.5538748168031 & 0.446125183196925 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]14.1312962202957[/C][C]-0.131296220295746[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.4719062198797[/C][C]2.52809378012027[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.0600224759325[/C][C]-3.06002247593248[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.7619833829419[/C][C]-2.76198338294189[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.4858556460747[/C][C]4.51414435392527[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.7046542420493[/C][C]3.29534575795074[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.9006807472853[/C][C]3.09931925271466[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.2166338141353[/C][C]-1.2166338141353[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.3964053161856[/C][C]-0.396405316185608[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.5861498928375[/C][C]0.413850107162524[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.7075516703123[/C][C]1.29244832968771[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.73276744227[/C][C]3.26723255772999[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.5632603830827[/C][C]-3.5632603830827[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.652839517453[/C][C]2.34716048254699[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.8674984690525[/C][C]2.13250153094747[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.5518918777609[/C][C]0.448108122239102[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.0597227426586[/C][C]-0.0597227426585944[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.8942058134817[/C][C]1.10579418651834[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.4970474040916[/C][C]-1.49704740409165[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.9382546465006[/C][C]2.06174535349944[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.4765350575034[/C][C]2.5234649424966[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.8414939806096[/C][C]-2.84149398060961[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.5109140015346[/C][C]-0.510914001534607[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.6674657051422[/C][C]-1.66746570514217[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4972295437932[/C][C]1.50277045620676[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.0287131008473[/C][C]-7.02871310084732[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.2410342027108[/C][C]0.758965797289226[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.4093049393965[/C][C]0.590695060603495[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.1021568616272[/C][C]0.897843138372813[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]14.0787450085401[/C][C]-3.07874500854006[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.8359885416367[/C][C]0.164011458363326[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.8817412800445[/C][C]0.118258719955501[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.2361413559711[/C][C]1.76385864402891[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.3693418945039[/C][C]-0.369341894503936[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.1354264781698[/C][C]-0.13542647816976[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.728884205218[/C][C]0.271115794781976[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]11.0895947421409[/C][C]-2.0895947421409[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.4463738949373[/C][C]0.55362610506266[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.4800252304662[/C][C]1.51997476953382[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.3346635915881[/C][C]-2.33466359158809[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.815213600011[/C][C]-0.815213600011012[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.8066644339493[/C][C]2.19333556605071[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.8032418706183[/C][C]0.196758129381705[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.2682253844808[/C][C]-1.26822538448081[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]14.7378893980036[/C][C]0.262110601996426[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.754802161886[/C][C]-2.75480216188597[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.3754918585291[/C][C]-0.375491858529096[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9864094770355[/C][C]0.0135905229644654[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.6530215785682[/C][C]3.34697842143183[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.8527028883008[/C][C]-1.85270288830083[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.279768652286[/C][C]0.720231347713954[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.5801129918821[/C][C]0.419887008117888[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.834309479386[/C][C]-0.834309479386016[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.6038791937884[/C][C]-1.60387919378844[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.3301960725687[/C][C]-2.33019607256873[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]13.8290421620091[/C][C]1.17095783799093[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.2654751066375[/C][C]1.73452489336253[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.4900968280511[/C][C]-0.490096828051089[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.2824061625817[/C][C]-3.28240616258172[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.4834199973343[/C][C]-1.48341999733432[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.9217329545057[/C][C]-2.92173295450565[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.6794130896755[/C][C]-1.6794130896755[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.6853506692265[/C][C]-3.68535066922648[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.158862078653[/C][C]0.841137921346952[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.6570259946684[/C][C]1.34297400533162[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.1754043338347[/C][C]-5.17540433383466[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.6693261666038[/C][C]-1.66932616660384[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.6380214856697[/C][C]-2.63802148566966[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.6713562577835[/C][C]1.32864374221648[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.7418475681764[/C][C]1.25815243182359[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.6892049283574[/C][C]0.310795071642587[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.8518533815601[/C][C]3.14814661843988[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.5187534501221[/C][C]0.481246549877948[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.3180006421193[/C][C]-0.318000642119303[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.0697092574661[/C][C]-2.06970925746606[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.312617716614[/C][C]-0.31261771661396[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.069584871921[/C][C]2.93041512807897[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.5846159275025[/C][C]0.415384072497524[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.9340197168277[/C][C]1.06598028317232[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.9937704308892[/C][C]-1.99377043088919[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.0182090181151[/C][C]-0.0182090181151229[/C][/ROW]
[ROW][C]82[/C][C]15[/C][C]15.5435911501378[/C][C]-0.543591150137809[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]14.3864633474583[/C][C]1.61353665254167[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.3872680901252[/C][C]0.612731909874834[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]14.2357265998029[/C][C]-0.235726599802869[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]14.1611127903906[/C][C]0.83888720960939[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.3462530464131[/C][C]-0.34625304641315[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]12.8328686178035[/C][C]0.167131382196479[/C][/ROW]
[ROW][C]89[/C][C]7[/C][C]10.6660549229509[/C][C]-3.66605492295089[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]13.8712308478821[/C][C]3.12876915211792[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.8198210076684[/C][C]0.180178992331609[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.263201296781[/C][C]0.73679870321897[/C][/ROW]
[ROW][C]93[/C][C]14[/C][C]13.4216639674575[/C][C]0.578336032542457[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.1179407101022[/C][C]-1.11794071010218[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.0691651470689[/C][C]0.930834852931136[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]12.8551358239965[/C][C]-0.85513582399649[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]14.9702205042097[/C][C]-0.970220504209678[/C][/ROW]
[ROW][C]98[/C][C]17[/C][C]14.9839352500113[/C][C]2.01606474998865[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]15.1235343193277[/C][C]-0.12353431932769[/C][/ROW]
[ROW][C]100[/C][C]17[/C][C]15.1637712803794[/C][C]1.83622871962063[/C][/ROW]
[ROW][C]101[/C][C]12[/C][C]13.0060030469496[/C][C]-1.00600304694957[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9678482983083[/C][C]1.03215170169169[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]14.435611491736[/C][C]-3.43561149173603[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]13.1859095451292[/C][C]1.81409045487076[/C][/ROW]
[ROW][C]105[/C][C]9[/C][C]11.4705270024897[/C][C]-2.47052700248975[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]14.914957252963[/C][C]1.08504274703701[/C][/ROW]
[ROW][C]107[/C][C]15[/C][C]12.953429822873[/C][C]2.04657017712703[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]12.8364320573179[/C][C]-2.83643205731788[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]9.29453277123379[/C][C]0.705467228766212[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]13.9340081754428[/C][C]1.06599182455718[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]13.204568185533[/C][C]-2.20456818553296[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]15.2907485701586[/C][C]-2.29074857015857[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]12.1157413224576[/C][C]1.88425867754243[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]14.1646943835229[/C][C]3.83530561647707[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]15.5056845471463[/C][C]0.4943154528537[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.9816502739951[/C][C]1.01834972600493[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.8508382295939[/C][C]0.149161770406144[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]15.0931700206499[/C][C]-1.09317002064993[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7127228076477[/C][C]0.287277192352314[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.5542623053079[/C][C]-0.554262305307939[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]13.535380630453[/C][C]0.464619369547046[/C][/ROW]
[ROW][C]122[/C][C]15[/C][C]14.9062912274024[/C][C]0.0937087725975806[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.8050964130047[/C][C]-0.805096413004664[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.5064346772957[/C][C]0.4935653227043[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]14.6814390556228[/C][C]-1.68143905562279[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]16.1462864582757[/C][C]0.853713541724324[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.2313564447104[/C][C]1.7686435552896[/C][/ROW]
[ROW][C]128[/C][C]19[/C][C]14.8856787019788[/C][C]4.11432129802116[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.5821264462733[/C][C]1.41787355372671[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]14.6241070911552[/C][C]-1.62410709115517[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]10.6978162028774[/C][C]-1.69781620287735[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]15.2726372545933[/C][C]-0.272637254593344[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]12.601107757815[/C][C]2.39889224218498[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.1681629982023[/C][C]0.83183700179766[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]13.6888328874185[/C][C]2.31116711258149[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]9.47399630709481[/C][C]1.52600369290518[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.2175967195348[/C][C]0.782403280465168[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.9530550243485[/C][C]-0.953055024348489[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.1645581657312[/C][C]0.835441834268822[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]13.8148456662453[/C][C]-0.814845666245294[/C][/ROW]
[ROW][C]141[/C][C]15[/C][C]14.5725305709401[/C][C]0.427469429059923[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.824795917337[/C][C]2.17520408266303[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]14.5279517852857[/C][C]-0.527951785285674[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]14.2018061288942[/C][C]0.798193871105817[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6069574678214[/C][C]1.39304253217858[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.3130713341458[/C][C]1.68692866585418[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.2765063740308[/C][C]-2.2765063740308[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]14.5576084393201[/C][C]-2.55760843932013[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]11.4962658480754[/C][C]-2.49626584807536[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.2446700217291[/C][C]1.75532997827087[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]12.4728218862782[/C][C]0.527178113721773[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.3489363386322[/C][C]0.651063661367826[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.4068591757054[/C][C]-2.40685917570542[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]11.5186191731159[/C][C]-2.5186191731159[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]11.7323222572939[/C][C]1.2676777427061[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.535621139704[/C][C]0.464378860295996[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.3123596909164[/C][C]0.687640309083596[/C][/ROW]
[ROW][C]158[/C][C]19[/C][C]14.7545095321491[/C][C]4.24549046785088[/C][/ROW]
[ROW][C]159[/C][C]13[/C][C]15.5175166821899[/C][C]-2.51751668218992[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.1117478148956[/C][C]-0.111747814895645[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.4997624117201[/C][C]0.500237588279941[/C][/ROW]
[ROW][C]162[/C][C]10[/C][C]9.32449694054531[/C][C]0.675503059454692[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]13.3144107331895[/C][C]0.68558926681053[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]11.4822897804962[/C][C]4.51771021950381[/C][/ROW]
[ROW][C]165[/C][C]10[/C][C]11.9709953926978[/C][C]-1.97099539269777[/C][/ROW]
[ROW][C]166[/C][C]11[/C][C]9.21648955031548[/C][C]1.78351044968452[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.0815224889261[/C][C]-0.0815224889260541[/C][/ROW]
[ROW][C]168[/C][C]12[/C][C]12.8647108994313[/C][C]-0.864710899431311[/C][/ROW]
[ROW][C]169[/C][C]9[/C][C]12.6920387934627[/C][C]-3.69203879346273[/C][/ROW]
[ROW][C]170[/C][C]9[/C][C]11.9437961706916[/C][C]-2.9437961706916[/C][/ROW]
[ROW][C]171[/C][C]11[/C][C]10.6181532601599[/C][C]0.381846739840127[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]14.0980027627333[/C][C]1.90199723726668[/C][/ROW]
[ROW][C]173[/C][C]9[/C][C]13.9828942091387[/C][C]-4.98289420913873[/C][/ROW]
[ROW][C]174[/C][C]13[/C][C]11.3816450333597[/C][C]1.61835496664031[/C][/ROW]
[ROW][C]175[/C][C]16[/C][C]13.289672699233[/C][C]2.71032730076699[/C][/ROW]
[ROW][C]176[/C][C]13[/C][C]15.2349826631287[/C][C]-2.23498266312869[/C][/ROW]
[ROW][C]177[/C][C]9[/C][C]12.2948552787294[/C][C]-3.29485527872945[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]11.5048150953963[/C][C]0.49518490460367[/C][/ROW]
[ROW][C]179[/C][C]16[/C][C]14.4754655029971[/C][C]1.52453449700285[/C][/ROW]
[ROW][C]180[/C][C]11[/C][C]13.176279864013[/C][C]-2.17627986401299[/C][/ROW]
[ROW][C]181[/C][C]14[/C][C]13.9367454399603[/C][C]0.063254560039674[/C][/ROW]
[ROW][C]182[/C][C]13[/C][C]14.714166390727[/C][C]-1.71416639072702[/C][/ROW]
[ROW][C]183[/C][C]15[/C][C]14.4736922810776[/C][C]0.526307718922366[/C][/ROW]
[ROW][C]184[/C][C]14[/C][C]14.8394566483366[/C][C]-0.839456648336643[/C][/ROW]
[ROW][C]185[/C][C]16[/C][C]14.042807752142[/C][C]1.95719224785799[/C][/ROW]
[ROW][C]186[/C][C]13[/C][C]11.5946346185537[/C][C]1.40536538144631[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]13.4435106100424[/C][C]0.556489389957552[/C][/ROW]
[ROW][C]188[/C][C]15[/C][C]14.2006935957299[/C][C]0.799306404270118[/C][/ROW]
[ROW][C]189[/C][C]13[/C][C]12.4629940782925[/C][C]0.537005921707511[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.2951557877057[/C][C]0.704844212294319[/C][/ROW]
[ROW][C]191[/C][C]11[/C][C]12.5791052858587[/C][C]-1.57910528585867[/C][/ROW]
[ROW][C]192[/C][C]14[/C][C]14.8517668888585[/C][C]-0.851766888858504[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]12.7671342006358[/C][C]2.2328657993642[/C][/ROW]
[ROW][C]194[/C][C]11[/C][C]12.3301311890193[/C][C]-1.3301311890193[/C][/ROW]
[ROW][C]195[/C][C]15[/C][C]13.0279527014879[/C][C]1.97204729851206[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]13.8741999018694[/C][C]-1.87419990186943[/C][/ROW]
[ROW][C]197[/C][C]14[/C][C]11.6730102441073[/C][C]2.32698975589266[/C][/ROW]
[ROW][C]198[/C][C]14[/C][C]13.2861233331282[/C][C]0.71387666687176[/C][/ROW]
[ROW][C]199[/C][C]8[/C][C]11.0779887888766[/C][C]-3.0779887888766[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]13.6559360679777[/C][C]-0.655936067977739[/C][/ROW]
[ROW][C]201[/C][C]9[/C][C]11.9887947071115[/C][C]-2.98879470711151[/C][/ROW]
[ROW][C]202[/C][C]15[/C][C]13.6735469389502[/C][C]1.32645306104983[/C][/ROW]
[ROW][C]203[/C][C]17[/C][C]14.01729738088[/C][C]2.98270261911998[/C][/ROW]
[ROW][C]204[/C][C]13[/C][C]12.3744304662106[/C][C]0.625569533789382[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]14.3239197400503[/C][C]0.676080259949742[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]13.5757186089573[/C][C]1.42428139104268[/C][/ROW]
[ROW][C]207[/C][C]14[/C][C]14.3157491850157[/C][C]-0.315749185015666[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]12.3219044901977[/C][C]3.67809550980233[/C][/ROW]
[ROW][C]209[/C][C]13[/C][C]12.7751587390889[/C][C]0.224841260911094[/C][/ROW]
[ROW][C]210[/C][C]16[/C][C]14.1778805416327[/C][C]1.8221194583673[/C][/ROW]
[ROW][C]211[/C][C]9[/C][C]11.535941698373[/C][C]-2.53594169837301[/C][/ROW]
[ROW][C]212[/C][C]16[/C][C]14.3696253482942[/C][C]1.63037465170582[/C][/ROW]
[ROW][C]213[/C][C]11[/C][C]12.1129575709745[/C][C]-1.11295757097455[/C][/ROW]
[ROW][C]214[/C][C]10[/C][C]13.7136826522495[/C][C]-3.71368265224954[/C][/ROW]
[ROW][C]215[/C][C]11[/C][C]11.9503623541785[/C][C]-0.950362354178522[/C][/ROW]
[ROW][C]216[/C][C]15[/C][C]13.0991337507538[/C][C]1.90086624924625[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.6840280903125[/C][C]2.31597190968754[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]14.0620379676766[/C][C]-0.0620379676766466[/C][/ROW]
[ROW][C]219[/C][C]8[/C][C]9.86871358917287[/C][C]-1.86871358917287[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]13.3710557963494[/C][C]1.62894420365057[/C][/ROW]
[ROW][C]221[/C][C]11[/C][C]13.6922783212302[/C][C]-2.69227832123023[/C][/ROW]
[ROW][C]222[/C][C]16[/C][C]13.4125116104209[/C][C]2.58748838957906[/C][/ROW]
[ROW][C]223[/C][C]10[/C][C]11.9223404037777[/C][C]-1.92234040377772[/C][/ROW]
[ROW][C]224[/C][C]15[/C][C]14.6526369499989[/C][C]0.347363050001121[/C][/ROW]
[ROW][C]225[/C][C]9[/C][C]9.57396659860397[/C][C]-0.573966598603964[/C][/ROW]
[ROW][C]226[/C][C]16[/C][C]14.2672202913509[/C][C]1.73277970864909[/C][/ROW]
[ROW][C]227[/C][C]19[/C][C]13.5776061666492[/C][C]5.42239383335085[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]13.5120983100328[/C][C]-1.51209831003284[/C][/ROW]
[ROW][C]229[/C][C]8[/C][C]9.36785631322598[/C][C]-1.36785631322598[/C][/ROW]
[ROW][C]230[/C][C]11[/C][C]13.1776025941417[/C][C]-2.17760259414166[/C][/ROW]
[ROW][C]231[/C][C]14[/C][C]13.6835255536223[/C][C]0.316474446377701[/C][/ROW]
[ROW][C]232[/C][C]9[/C][C]11.8450880518024[/C][C]-2.84508805180244[/C][/ROW]
[ROW][C]233[/C][C]15[/C][C]14.7033991902502[/C][C]0.296600809749783[/C][/ROW]
[ROW][C]234[/C][C]13[/C][C]12.0866785963915[/C][C]0.913321403608521[/C][/ROW]
[ROW][C]235[/C][C]16[/C][C]14.6609592911363[/C][C]1.33904070886374[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]12.6330328652547[/C][C]-1.63303286525471[/C][/ROW]
[ROW][C]237[/C][C]12[/C][C]11.2602661547166[/C][C]0.739733845283366[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]12.5090387365518[/C][C]0.490961263448215[/C][/ROW]
[ROW][C]239[/C][C]10[/C][C]14.0724433581468[/C][C]-4.07244335814679[/C][/ROW]
[ROW][C]240[/C][C]11[/C][C]13.4002282956596[/C][C]-2.40022829565964[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]14.6581615360238[/C][C]-2.65816153602382[/C][/ROW]
[ROW][C]242[/C][C]8[/C][C]10.4533901914072[/C][C]-2.45339019140721[/C][/ROW]
[ROW][C]243[/C][C]12[/C][C]11.6654380204318[/C][C]0.334561979568206[/C][/ROW]
[ROW][C]244[/C][C]12[/C][C]12.0935692280008[/C][C]-0.0935692280008212[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.2366492468646[/C][C]1.76335075313539[/C][/ROW]
[ROW][C]246[/C][C]11[/C][C]10.5780420193572[/C][C]0.421957980642801[/C][/ROW]
[ROW][C]247[/C][C]13[/C][C]12.4875441744348[/C][C]0.512455825565181[/C][/ROW]
[ROW][C]248[/C][C]14[/C][C]8.62229773979479[/C][C]5.37770226020521[/C][/ROW]
[ROW][C]249[/C][C]10[/C][C]10.0270819103525[/C][C]-0.02708191035249[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.3456895930049[/C][C]0.65431040699507[/C][/ROW]
[ROW][C]251[/C][C]15[/C][C]12.6910507425476[/C][C]2.30894925745245[/C][/ROW]
[ROW][C]252[/C][C]13[/C][C]11.7147504775867[/C][C]1.28524952241327[/C][/ROW]
[ROW][C]253[/C][C]13[/C][C]13.9201904658877[/C][C]-0.920190465887744[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]13.4123285596985[/C][C]-0.412328559698469[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]11.525197308205[/C][C]0.474802691794957[/C][/ROW]
[ROW][C]256[/C][C]12[/C][C]12.3357068813777[/C][C]-0.335706881377749[/C][/ROW]
[ROW][C]257[/C][C]9[/C][C]10.5582364042597[/C][C]-1.55823640425966[/C][/ROW]
[ROW][C]258[/C][C]9[/C][C]11.1965521315279[/C][C]-2.1965521315279[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]12.2619126167435[/C][C]2.73808738325654[/C][/ROW]
[ROW][C]260[/C][C]10[/C][C]14.6825109321869[/C][C]-4.6825109321869[/C][/ROW]
[ROW][C]261[/C][C]14[/C][C]13.3413703444173[/C][C]0.658629655582655[/C][/ROW]
[ROW][C]262[/C][C]15[/C][C]13.1979782517988[/C][C]1.80202174820116[/C][/ROW]
[ROW][C]263[/C][C]7[/C][C]9.59724048536167[/C][C]-2.59724048536167[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]13.5538748168031[/C][C]0.446125183196925[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.1312962202957-0.131296220295746
21815.47190621987972.52809378012027
31114.0600224759325-3.06002247593248
41214.7619833829419-2.76198338294189
51611.48585564607474.51414435392527
61814.70465424204933.29534575795074
71410.90068074728533.09931925271466
81415.2166338141353-1.2166338141353
91515.3964053161856-0.396405316185608
101514.58614989283750.413850107162524
111715.70755167031231.29244832968771
121915.732767442273.26723255772999
131013.5632603830827-3.5632603830827
141613.6528395174532.34716048254699
151815.86749846905252.13250153094747
161413.55189187776090.448108122239102
171414.0597227426586-0.0597227426585944
181715.89420581348171.10579418651834
191415.4970474040916-1.49704740409165
201613.93825464650062.06174535349944
211815.47653505750342.5234649424966
221113.8414939806096-2.84149398060961
231414.5109140015346-0.510914001534607
241213.6674657051422-1.66746570514217
251715.49722954379321.50277045620676
26916.0287131008473-7.02871310084732
271615.24103420271080.758965797289226
281413.40930493939650.590695060603495
291514.10215686162720.897843138372813
301114.0787450085401-3.07874500854006
311615.83598854163670.164011458363326
321312.88174128004450.118258719955501
331715.23614135597111.76385864402891
341515.3693418945039-0.369341894503936
351414.1354264781698-0.13542647816976
361615.7288842052180.271115794781976
37911.0895947421409-2.0895947421409
381514.44637389493730.55362610506266
391715.48002523046621.51997476953382
401315.3346635915881-2.33466359158809
411515.815213600011-0.815213600011012
421613.80666443394932.19333556605071
431615.80324187061830.196758129381705
441213.2682253844808-1.26822538448081
451514.73788939800360.262110601996426
461113.754802161886-2.75480216188597
471515.3754918585291-0.375491858529096
481514.98640947703550.0135905229644654
491713.65302157856823.34697842143183
501314.8527028883008-1.85270288830083
511615.2797686522860.720231347713954
521413.58011299188210.419887008117888
531111.834309479386-0.834309479386016
541213.6038791937884-1.60387919378844
551214.3301960725687-2.33019607256873
561513.82904216200911.17095783799093
571614.26547510663751.73452489336253
581515.4900968280511-0.490096828051089
591215.2824061625817-3.28240616258172
601213.4834199973343-1.48341999733432
61810.9217329545057-2.92173295450565
621314.6794130896755-1.6794130896755
631114.6853506692265-3.68535066922648
641413.1588620786530.841137921346952
651513.65702599466841.34297400533162
661015.1754043338347-5.17540433383466
671112.6693261666038-1.66932616660384
681214.6380214856697-2.63802148566966
691513.67135625778351.32864374221648
701513.74184756817641.25815243182359
711413.68920492835740.310795071642587
721612.85185338156013.14814661843988
731514.51875345012210.481246549877948
741515.3180006421193-0.318000642119303
751315.0697092574661-2.06970925746606
761212.312617716614-0.31261771661396
771714.0695848719212.93041512807897
781312.58461592750250.415384072497524
791513.93401971682771.06598028317232
801314.9937704308892-1.99377043088919
811515.0182090181151-0.0182090181151229
821515.5435911501378-0.543591150137809
831614.38646334745831.61353665254167
841514.38726809012520.612731909874834
851414.2357265998029-0.235726599802869
861514.16111279039060.83888720960939
871414.3462530464131-0.34625304641315
881312.83286861780350.167131382196479
89710.6660549229509-3.66605492295089
901713.87123084788213.12876915211792
911312.81982100766840.180178992331609
921514.2632012967810.73679870321897
931413.42166396745750.578336032542457
941314.1179407101022-1.11794071010218
951615.06916514706890.930834852931136
961212.8551358239965-0.85513582399649
971414.9702205042097-0.970220504209678
981714.98393525001132.01606474998865
991515.1235343193277-0.12353431932769
1001715.16377128037941.83622871962063
1011213.0060030469496-1.00600304694957
1021614.96784829830831.03215170169169
1031114.435611491736-3.43561149173603
1041513.18590954512921.81409045487076
105911.4705270024897-2.47052700248975
1061614.9149572529631.08504274703701
1071512.9534298228732.04657017712703
1081012.8364320573179-2.83643205731788
109109.294532771233790.705467228766212
1101513.93400817544281.06599182455718
1111113.204568185533-2.20456818553296
1121315.2907485701586-2.29074857015857
1131412.11574132245761.88425867754243
1141814.16469438352293.83530561647707
1151615.50568454714630.4943154528537
1161412.98165027399511.01834972600493
1171413.85083822959390.149161770406144
1181415.0931700206499-1.09317002064993
1191413.71272280764770.287277192352314
1201212.5542623053079-0.554262305307939
1211413.5353806304530.464619369547046
1221514.90629122740240.0937087725975806
1231515.8050964130047-0.805096413004664
1241514.50643467729570.4935653227043
1251314.6814390556228-1.68143905562279
1261716.14628645827570.853713541724324
1271715.23135644471041.7686435552896
1281914.88567870197884.11432129802116
1291513.58212644627331.41787355372671
1301314.6241070911552-1.62410709115517
131910.6978162028774-1.69781620287735
1321515.2726372545933-0.272637254593344
1331512.6011077578152.39889224218498
1341514.16816299820230.83183700179766
1351613.68883288741852.31116711258149
136119.473996307094811.52600369290518
1371413.21759671953480.782403280465168
1381111.9530550243485-0.953055024348489
1391514.16455816573120.835441834268822
1401313.8148456662453-0.814845666245294
1411514.57253057094010.427469429059923
1421613.8247959173372.17520408266303
1431414.5279517852857-0.527951785285674
1441514.20180612889420.798193871105817
1451614.60695746782141.39304253217858
1461614.31307133414581.68692866585418
1471113.2765063740308-2.2765063740308
1481214.5576084393201-2.55760843932013
149911.4962658480754-2.49626584807536
1501614.24467002172911.75532997827087
1511312.47282188627820.527178113721773
1521615.34893633863220.651063661367826
1531214.4068591757054-2.40685917570542
154911.5186191731159-2.5186191731159
1551311.73232225729391.2676777427061
1561312.5356211397040.464378860295996
1571413.31235969091640.687640309083596
1581914.75450953214914.24549046785088
1591315.5175166821899-2.51751668218992
1601212.1117478148956-0.111747814895645
1611312.49976241172010.500237588279941
162109.324496940545310.675503059454692
1631413.31441073318950.68558926681053
1641611.48228978049624.51771021950381
1651011.9709953926978-1.97099539269777
166119.216489550315481.78351044968452
1671414.0815224889261-0.0815224889260541
1681212.8647108994313-0.864710899431311
169912.6920387934627-3.69203879346273
170911.9437961706916-2.9437961706916
1711110.61815326015990.381846739840127
1721614.09800276273331.90199723726668
173913.9828942091387-4.98289420913873
1741311.38164503335971.61835496664031
1751613.2896726992332.71032730076699
1761315.2349826631287-2.23498266312869
177912.2948552787294-3.29485527872945
1781211.50481509539630.49518490460367
1791614.47546550299711.52453449700285
1801113.176279864013-2.17627986401299
1811413.93674543996030.063254560039674
1821314.714166390727-1.71416639072702
1831514.47369228107760.526307718922366
1841414.8394566483366-0.839456648336643
1851614.0428077521421.95719224785799
1861311.59463461855371.40536538144631
1871413.44351061004240.556489389957552
1881514.20069359572990.799306404270118
1891312.46299407829250.537005921707511
1901110.29515578770570.704844212294319
1911112.5791052858587-1.57910528585867
1921414.8517668888585-0.851766888858504
1931512.76713420063582.2328657993642
1941112.3301311890193-1.3301311890193
1951513.02795270148791.97204729851206
1961213.8741999018694-1.87419990186943
1971411.67301024410732.32698975589266
1981413.28612333312820.71387666687176
199811.0779887888766-3.0779887888766
2001313.6559360679777-0.655936067977739
201911.9887947071115-2.98879470711151
2021513.67354693895021.32645306104983
2031714.017297380882.98270261911998
2041312.37443046621060.625569533789382
2051514.32391974005030.676080259949742
2061513.57571860895731.42428139104268
2071414.3157491850157-0.315749185015666
2081612.32190449019773.67809550980233
2091312.77515873908890.224841260911094
2101614.17788054163271.8221194583673
211911.535941698373-2.53594169837301
2121614.36962534829421.63037465170582
2131112.1129575709745-1.11295757097455
2141013.7136826522495-3.71368265224954
2151111.9503623541785-0.950362354178522
2161513.09913375075381.90086624924625
2171714.68402809031252.31597190968754
2181414.0620379676766-0.0620379676766466
21989.86871358917287-1.86871358917287
2201513.37105579634941.62894420365057
2211113.6922783212302-2.69227832123023
2221613.41251161042092.58748838957906
2231011.9223404037777-1.92234040377772
2241514.65263694999890.347363050001121
22599.57396659860397-0.573966598603964
2261614.26722029135091.73277970864909
2271913.57760616664925.42239383335085
2281213.5120983100328-1.51209831003284
22989.36785631322598-1.36785631322598
2301113.1776025941417-2.17760259414166
2311413.68352555362230.316474446377701
232911.8450880518024-2.84508805180244
2331514.70339919025020.296600809749783
2341312.08667859639150.913321403608521
2351614.66095929113631.33904070886374
2361112.6330328652547-1.63303286525471
2371211.26026615471660.739733845283366
2381312.50903873655180.490961263448215
2391014.0724433581468-4.07244335814679
2401113.4002282956596-2.40022829565964
2411214.6581615360238-2.65816153602382
242810.4533901914072-2.45339019140721
2431211.66543802043180.334561979568206
2441212.0935692280008-0.0935692280008212
2451513.23664924686461.76335075313539
2461110.57804201935720.421957980642801
2471312.48754417443480.512455825565181
248148.622297739794795.37770226020521
2491010.0270819103525-0.02708191035249
2501211.34568959300490.65431040699507
2511512.69105074254762.30894925745245
2521311.71475047758671.28524952241327
2531313.9201904658877-0.920190465887744
2541313.4123285596985-0.412328559698469
2551211.5251973082050.474802691794957
2561212.3357068813777-0.335706881377749
257910.5582364042597-1.55823640425966
258911.1965521315279-2.1965521315279
2591512.26191261674352.73808738325654
2601014.6825109321869-4.6825109321869
2611413.34137034441730.658629655582655
2621513.19797825179881.80202174820116
26379.59724048536167-2.59724048536167
2641413.55387481680310.446125183196925






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04250684386230720.08501368772461440.957493156137693
120.7947008382390560.4105983235218870.205299161760944
130.982412874862430.03517425027514070.0175871251375704
140.9807202822397430.03855943552051320.0192797177602566
150.9811949759322550.03761004813548910.0188050240677445
160.9697252059419060.06054958811618780.0302747940580939
170.9655493829673970.06890123406520620.0344506170326031
180.9492474395609440.1015051208781120.0507525604390562
190.9337558621067140.1324882757865730.0662441378932863
200.9183583358816880.1632833282366250.0816416641183124
210.9158989910270980.1682020179458040.0841010089729021
220.9648211212759560.07035775744808830.0351788787240442
230.9505054334441420.09898913311171680.0494945665558584
240.9388864820629940.1222270358740120.0611135179370062
250.9188049609103810.1623900781792380.081195039089619
260.998506017215630.002987965568739160.00149398278436958
270.9979454147082410.004109170583517590.00205458529175879
280.9968182695991940.00636346080161220.0031817304008061
290.9953531444880570.009293711023885030.00464685551194252
300.9963023299212360.007395340157527660.00369767007876383
310.9947766539002340.01044669219953140.00522334609976569
320.992288764390310.01542247121938050.00771123560969024
330.992192196018250.01561560796349940.00780780398174971
340.9887581653080290.02248366938394180.0112418346919709
350.9841445813859110.0317108372281790.0158554186140895
360.9783358164735670.04332836705286670.0216641835264333
370.9806009136403280.03879817271934490.0193990863596724
380.9751162314213540.04976753715729290.0248837685786465
390.9747070731450740.0505858537098530.0252929268549265
400.971367549058430.057264901883140.02863245094157
410.9622274096789920.07554518064201590.037772590321008
420.9649624824951530.07007503500969420.0350375175048471
430.9570058430589510.08598831388209780.0429941569410489
440.9462814874526870.1074370250946270.0537185125473134
450.9321580202377230.1356839595245540.0678419797622771
460.9345895483544260.1308209032911490.0654104516455744
470.9211089945555320.1577820108889350.0788910054444675
480.9041116313953580.1917767372092840.0958883686046418
490.9387063008979070.1225873982041870.0612936991020933
500.9316369750909920.1367260498180170.0683630249090084
510.9198029956205110.1603940087589780.0801970043794888
520.9029152197083190.1941695605833620.097084780291681
530.8845001286318020.2309997427363960.115499871368198
540.8696569354903740.2606861290192520.130343064509626
550.8587066201960010.2825867596079970.141293379803999
560.8442489801471280.3115020397057440.155751019852872
570.838815682846030.3223686343079410.16118431715397
580.8110128294476510.3779743411046980.188987170552349
590.8354513914490880.3290972171018230.164548608550912
600.8189417638696280.3621164722607440.181058236130372
610.8302781267224010.3394437465551980.169721873277599
620.812956207511390.3740875849772210.18704379248861
630.8505595771551510.2988808456896970.149440422844849
640.8477362974496540.3045274051006920.152263702550346
650.8498130891981980.3003738216036040.150186910801802
660.9228470068378940.1543059863242120.0771529931621059
670.9118797598522520.1762404802954950.0881202401477475
680.9117773962569510.1764452074860980.088222603743049
690.9151409888320310.1697180223359390.0848590111679693
700.9150834712575110.1698330574849790.0849165287424895
710.9022530626517470.1954938746965060.097746937348253
720.9332176620899110.1335646758201770.0667823379100886
730.9227029464356950.1545941071286110.0772970535643055
740.9077743539920480.1844512920159040.0922256460079518
750.8990966553550270.2018066892899450.100903344644973
760.8816020106499020.2367959787001960.118397989350098
770.9097707023082430.1804585953835150.0902292976917573
780.8959308757266510.2081382485466980.104069124273349
790.8899835669833640.2200328660332720.110016433016636
800.8820128996635510.2359742006728980.117987100336449
810.8630669609478560.2738660781042890.136933039052144
820.8421889505595420.3156220988809150.157811049440458
830.8423175447802820.3153649104394350.157682455219718
840.8241002381847320.3517995236305360.175899761815268
850.7998811446838030.4002377106323930.200118855316197
860.7764911827949220.4470176344101570.223508817205078
870.7480016770706710.5039966458586590.251998322929329
880.7189714502093570.5620570995812860.281028549790643
890.7872332721899490.4255334556201010.212766727810051
900.8255917846655550.348816430668890.174408215334445
910.8040579595726840.3918840808546320.195942040427316
920.779483683672240.441032632655520.22051631632776
930.7595222874856250.4809554250287510.240477712514375
940.735908151252790.528183697494420.26409184874721
950.7124861479844180.5750277040311650.287513852015582
960.6852693879991620.6294612240016760.314730612000838
970.6586535848247910.6826928303504180.341346415175209
980.6638882070726250.6722235858547510.336111792927375
990.629482856135170.7410342877296590.37051714386483
1000.6248459390457350.7503081219085290.375154060954264
1010.5991187751404240.8017624497191520.400881224859576
1020.5716928978185980.8566142043628030.428307102181402
1030.6247903272076790.7504193455846430.375209672792321
1040.611546719426610.776906561146780.38845328057339
1050.6283124452534170.7433751094931660.371687554746583
1060.6043618528912350.791276294217530.395638147108765
1070.597042340721360.8059153185572790.40295765927864
1080.6323369178066170.7353261643867660.367663082193383
1090.5990437366815250.8019125266369490.400956263318475
1100.5730601776244520.8538796447510960.426939822375548
1110.5776278478998880.8447443042002240.422372152100112
1120.6016790286478390.7966419427043230.398320971352161
1130.6083038157872470.7833923684255070.391696184212753
1140.6941641735239180.6116716529521650.305835826476082
1150.6662760906846750.6674478186306490.333723909315325
1160.6400873829026050.7198252341947890.359912617097395
1170.6064106241241890.7871787517516220.393589375875811
1180.5820574613266390.8358850773467220.417942538673361
1190.5465480898698910.9069038202602180.453451910130109
1200.5136090061117620.9727819877764770.486390993888238
1210.4879435407127560.9758870814255130.512056459287244
1220.4532025596816280.9064051193632570.546797440318372
1230.424670039155640.849340078311280.57532996084436
1240.3944636536108670.7889273072217330.605536346389133
1250.3849301321098890.7698602642197780.615069867890111
1260.3569692091877130.7139384183754260.643030790812287
1270.3419246869933990.6838493739867970.658075313006601
1280.445411884764580.8908237695291590.55458811523542
1290.4256597164886260.8513194329772520.574340283511374
1300.4150013898589290.8300027797178570.584998610141071
1310.4041285874477270.8082571748954530.595871412552273
1320.3723757232649240.7447514465298480.627624276735076
1330.392140010987290.784280021974580.60785998901271
1340.3623819932931880.7247639865863750.637618006706812
1350.3726194368809560.7452388737619110.627380563119044
1360.3620711747133630.7241423494267270.637928825286637
1370.3326230472216980.6652460944433960.667376952778302
1380.3089652260024050.6179304520048110.691034773997595
1390.2816647565673710.5633295131347420.718335243432629
1400.2581934477967950.516386895593590.741806552203205
1410.2310526035784730.4621052071569450.768947396421527
1420.2337224583725310.4674449167450620.766277541627469
1430.2088614089778410.4177228179556820.791138591022159
1440.1889998131361090.3779996262722180.811000186863891
1450.1738111709427120.3476223418854250.826188829057288
1460.1657921002223090.3315842004446190.834207899777691
1470.1747474936457710.3494949872915420.825252506354229
1480.1931974619769930.3863949239539870.806802538023007
1490.2130527181681710.4261054363363420.786947281831829
1500.2083429433710850.416685886742170.791657056628915
1510.1848803653695790.3697607307391570.815119634630421
1520.1639357132604350.327871426520870.836064286739565
1530.1757115971941250.3514231943882510.824288402805875
1540.1930345517953450.386069103590690.806965448204655
1550.177042996938380.354085993876760.82295700306162
1560.1565168345250160.3130336690500330.843483165474984
1570.1381629223446590.2763258446893170.861837077655341
1580.2207132602062570.4414265204125150.779286739793743
1590.2392220791178560.4784441582357110.760777920882144
1600.2156841827977830.4313683655955650.784315817202217
1610.1925291333478540.3850582666957080.807470866652146
1620.1703782048580320.3407564097160640.829621795141968
1630.1495733857356020.2991467714712030.850426614264398
1640.249228372517040.498456745034080.75077162748296
1650.244628458243210.4892569164864190.75537154175679
1660.2408738969968720.4817477939937440.759126103003128
1670.2131093156413770.4262186312827540.786890684358623
1680.1923440930678940.3846881861357880.807655906932106
1690.2467702983557830.4935405967115660.753229701644217
1700.2763007413527050.5526014827054110.723699258647295
1710.2471814239858090.4943628479716170.752818576014191
1720.2432053604690570.4864107209381140.756794639530943
1730.4106891331048650.821378266209730.589310866895135
1740.3922202263911880.7844404527823760.607779773608812
1750.4127702326521390.8255404653042790.587229767347861
1760.4246739145928920.8493478291857840.575326085407108
1770.4882419407985920.9764838815971850.511758059201408
1780.4513516650372390.9027033300744780.548648334962761
1790.4291066300088020.8582132600176040.570893369991198
1800.4366347895549240.8732695791098480.563365210445076
1810.4012524390327330.8025048780654670.598747560967267
1820.4053015672866760.8106031345733510.594698432713324
1830.3692487678781750.738497535756350.630751232121825
1840.3472226621900550.694445324380110.652777337809945
1850.3480092421153640.6960184842307280.651990757884636
1860.333256126733380.6665122534667610.66674387326662
1870.2988643545595540.5977287091191080.701135645440446
1880.267717472139730.535434944279460.73228252786027
1890.2368455724570050.4736911449140090.763154427542995
1900.2121690092534110.4243380185068220.787830990746589
1910.224964928071970.4499298561439390.77503507192803
1920.2080772506088470.4161545012176940.791922749391153
1930.2144369021853080.4288738043706170.785563097814692
1940.2054260103607650.410852020721530.794573989639235
1950.2019530870626660.4039061741253320.798046912937334
1960.2146874978918880.4293749957837770.785312502108112
1970.2534981619597610.5069963239195210.74650183804024
1980.2346826537817540.4693653075635070.765317346218246
1990.2707654891556870.5415309783113740.729234510844313
2000.2390154658728910.4780309317457830.760984534127109
2010.2800279558033090.5600559116066190.719972044196691
2020.2546535098170620.5093070196341240.745346490182938
2030.2921243444894560.5842486889789110.707875655510544
2040.2599692528373970.5199385056747940.740030747162603
2050.227457385891820.454914771783640.77254261410818
2060.2073974446900940.4147948893801880.792602555309906
2070.1774362254740760.3548724509481520.822563774525924
2080.2030277852340990.4060555704681980.796972214765901
2090.1732967549974680.3465935099949360.826703245002532
2100.1828799639333370.3657599278666730.817120036066663
2110.2023711422843920.4047422845687840.797628857715608
2120.1931381806064520.3862763612129030.806861819393548
2130.1677028113124410.3354056226248810.832297188687559
2140.2074971518786260.4149943037572530.792502848121374
2150.1844282933431830.3688565866863660.815571706656817
2160.1731703460036960.3463406920073920.826829653996304
2170.2035132974287380.4070265948574760.796486702571262
2180.1756349090145490.3512698180290990.824365090985451
2190.1622089469399110.3244178938798210.837791053060089
2200.1795855103434340.3591710206868680.820414489656566
2210.1840362780118840.3680725560237680.815963721988116
2220.1817467065487370.3634934130974730.818253293451263
2230.1651973969294270.3303947938588550.834802603070573
2240.1371223435273030.2742446870546060.862877656472697
2250.1345483607909140.2690967215818280.865451639209086
2260.1653466288016710.3306932576033430.834653371198329
2270.3936698989154160.7873397978308330.606330101084584
2280.3520697308810510.7041394617621020.647930269118949
2290.3131507198672050.626301439734410.686849280132795
2300.2872839911817250.574567982363450.712716008818275
2310.2510731528707380.5021463057414760.748926847129262
2320.2803204485920530.5606408971841060.719679551407947
2330.237437798561890.4748755971237790.76256220143811
2340.1992212244374120.3984424488748230.800778775562588
2350.2063151704318450.4126303408636910.793684829568155
2360.1780835168399390.3561670336798780.821916483160061
2370.1537059613478790.3074119226957580.846294038652121
2380.1184559542003740.2369119084007490.881544045799626
2390.2270572130685160.4541144261370330.772942786931484
2400.2370289267852390.4740578535704780.762971073214761
2410.2406917723757890.4813835447515790.759308227624211
2420.8584858668889790.2830282662220410.141514133111021
2430.8088026128872420.3823947742255160.191197387112758
2440.754986204316420.4900275913671590.24501379568358
2450.7014371646526130.5971256706947730.298562835347387
2460.6325393712391220.7349212575217570.367460628760878
2470.6468165383593540.7063669232812920.353183461640646
2480.6503902063299630.6992195873400730.349609793670037
2490.5449411173020320.9101177653959360.455058882697968
2500.4595641126730480.9191282253460950.540435887326952
2510.3999388678320990.7998777356641990.600061132167901
2520.9055946370019150.188810725996170.0944053629980852
2530.8175870620672150.3648258758655690.182412937932785

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0425068438623072 & 0.0850136877246144 & 0.957493156137693 \tabularnewline
12 & 0.794700838239056 & 0.410598323521887 & 0.205299161760944 \tabularnewline
13 & 0.98241287486243 & 0.0351742502751407 & 0.0175871251375704 \tabularnewline
14 & 0.980720282239743 & 0.0385594355205132 & 0.0192797177602566 \tabularnewline
15 & 0.981194975932255 & 0.0376100481354891 & 0.0188050240677445 \tabularnewline
16 & 0.969725205941906 & 0.0605495881161878 & 0.0302747940580939 \tabularnewline
17 & 0.965549382967397 & 0.0689012340652062 & 0.0344506170326031 \tabularnewline
18 & 0.949247439560944 & 0.101505120878112 & 0.0507525604390562 \tabularnewline
19 & 0.933755862106714 & 0.132488275786573 & 0.0662441378932863 \tabularnewline
20 & 0.918358335881688 & 0.163283328236625 & 0.0816416641183124 \tabularnewline
21 & 0.915898991027098 & 0.168202017945804 & 0.0841010089729021 \tabularnewline
22 & 0.964821121275956 & 0.0703577574480883 & 0.0351788787240442 \tabularnewline
23 & 0.950505433444142 & 0.0989891331117168 & 0.0494945665558584 \tabularnewline
24 & 0.938886482062994 & 0.122227035874012 & 0.0611135179370062 \tabularnewline
25 & 0.918804960910381 & 0.162390078179238 & 0.081195039089619 \tabularnewline
26 & 0.99850601721563 & 0.00298796556873916 & 0.00149398278436958 \tabularnewline
27 & 0.997945414708241 & 0.00410917058351759 & 0.00205458529175879 \tabularnewline
28 & 0.996818269599194 & 0.0063634608016122 & 0.0031817304008061 \tabularnewline
29 & 0.995353144488057 & 0.00929371102388503 & 0.00464685551194252 \tabularnewline
30 & 0.996302329921236 & 0.00739534015752766 & 0.00369767007876383 \tabularnewline
31 & 0.994776653900234 & 0.0104466921995314 & 0.00522334609976569 \tabularnewline
32 & 0.99228876439031 & 0.0154224712193805 & 0.00771123560969024 \tabularnewline
33 & 0.99219219601825 & 0.0156156079634994 & 0.00780780398174971 \tabularnewline
34 & 0.988758165308029 & 0.0224836693839418 & 0.0112418346919709 \tabularnewline
35 & 0.984144581385911 & 0.031710837228179 & 0.0158554186140895 \tabularnewline
36 & 0.978335816473567 & 0.0433283670528667 & 0.0216641835264333 \tabularnewline
37 & 0.980600913640328 & 0.0387981727193449 & 0.0193990863596724 \tabularnewline
38 & 0.975116231421354 & 0.0497675371572929 & 0.0248837685786465 \tabularnewline
39 & 0.974707073145074 & 0.050585853709853 & 0.0252929268549265 \tabularnewline
40 & 0.97136754905843 & 0.05726490188314 & 0.02863245094157 \tabularnewline
41 & 0.962227409678992 & 0.0755451806420159 & 0.037772590321008 \tabularnewline
42 & 0.964962482495153 & 0.0700750350096942 & 0.0350375175048471 \tabularnewline
43 & 0.957005843058951 & 0.0859883138820978 & 0.0429941569410489 \tabularnewline
44 & 0.946281487452687 & 0.107437025094627 & 0.0537185125473134 \tabularnewline
45 & 0.932158020237723 & 0.135683959524554 & 0.0678419797622771 \tabularnewline
46 & 0.934589548354426 & 0.130820903291149 & 0.0654104516455744 \tabularnewline
47 & 0.921108994555532 & 0.157782010888935 & 0.0788910054444675 \tabularnewline
48 & 0.904111631395358 & 0.191776737209284 & 0.0958883686046418 \tabularnewline
49 & 0.938706300897907 & 0.122587398204187 & 0.0612936991020933 \tabularnewline
50 & 0.931636975090992 & 0.136726049818017 & 0.0683630249090084 \tabularnewline
51 & 0.919802995620511 & 0.160394008758978 & 0.0801970043794888 \tabularnewline
52 & 0.902915219708319 & 0.194169560583362 & 0.097084780291681 \tabularnewline
53 & 0.884500128631802 & 0.230999742736396 & 0.115499871368198 \tabularnewline
54 & 0.869656935490374 & 0.260686129019252 & 0.130343064509626 \tabularnewline
55 & 0.858706620196001 & 0.282586759607997 & 0.141293379803999 \tabularnewline
56 & 0.844248980147128 & 0.311502039705744 & 0.155751019852872 \tabularnewline
57 & 0.83881568284603 & 0.322368634307941 & 0.16118431715397 \tabularnewline
58 & 0.811012829447651 & 0.377974341104698 & 0.188987170552349 \tabularnewline
59 & 0.835451391449088 & 0.329097217101823 & 0.164548608550912 \tabularnewline
60 & 0.818941763869628 & 0.362116472260744 & 0.181058236130372 \tabularnewline
61 & 0.830278126722401 & 0.339443746555198 & 0.169721873277599 \tabularnewline
62 & 0.81295620751139 & 0.374087584977221 & 0.18704379248861 \tabularnewline
63 & 0.850559577155151 & 0.298880845689697 & 0.149440422844849 \tabularnewline
64 & 0.847736297449654 & 0.304527405100692 & 0.152263702550346 \tabularnewline
65 & 0.849813089198198 & 0.300373821603604 & 0.150186910801802 \tabularnewline
66 & 0.922847006837894 & 0.154305986324212 & 0.0771529931621059 \tabularnewline
67 & 0.911879759852252 & 0.176240480295495 & 0.0881202401477475 \tabularnewline
68 & 0.911777396256951 & 0.176445207486098 & 0.088222603743049 \tabularnewline
69 & 0.915140988832031 & 0.169718022335939 & 0.0848590111679693 \tabularnewline
70 & 0.915083471257511 & 0.169833057484979 & 0.0849165287424895 \tabularnewline
71 & 0.902253062651747 & 0.195493874696506 & 0.097746937348253 \tabularnewline
72 & 0.933217662089911 & 0.133564675820177 & 0.0667823379100886 \tabularnewline
73 & 0.922702946435695 & 0.154594107128611 & 0.0772970535643055 \tabularnewline
74 & 0.907774353992048 & 0.184451292015904 & 0.0922256460079518 \tabularnewline
75 & 0.899096655355027 & 0.201806689289945 & 0.100903344644973 \tabularnewline
76 & 0.881602010649902 & 0.236795978700196 & 0.118397989350098 \tabularnewline
77 & 0.909770702308243 & 0.180458595383515 & 0.0902292976917573 \tabularnewline
78 & 0.895930875726651 & 0.208138248546698 & 0.104069124273349 \tabularnewline
79 & 0.889983566983364 & 0.220032866033272 & 0.110016433016636 \tabularnewline
80 & 0.882012899663551 & 0.235974200672898 & 0.117987100336449 \tabularnewline
81 & 0.863066960947856 & 0.273866078104289 & 0.136933039052144 \tabularnewline
82 & 0.842188950559542 & 0.315622098880915 & 0.157811049440458 \tabularnewline
83 & 0.842317544780282 & 0.315364910439435 & 0.157682455219718 \tabularnewline
84 & 0.824100238184732 & 0.351799523630536 & 0.175899761815268 \tabularnewline
85 & 0.799881144683803 & 0.400237710632393 & 0.200118855316197 \tabularnewline
86 & 0.776491182794922 & 0.447017634410157 & 0.223508817205078 \tabularnewline
87 & 0.748001677070671 & 0.503996645858659 & 0.251998322929329 \tabularnewline
88 & 0.718971450209357 & 0.562057099581286 & 0.281028549790643 \tabularnewline
89 & 0.787233272189949 & 0.425533455620101 & 0.212766727810051 \tabularnewline
90 & 0.825591784665555 & 0.34881643066889 & 0.174408215334445 \tabularnewline
91 & 0.804057959572684 & 0.391884080854632 & 0.195942040427316 \tabularnewline
92 & 0.77948368367224 & 0.44103263265552 & 0.22051631632776 \tabularnewline
93 & 0.759522287485625 & 0.480955425028751 & 0.240477712514375 \tabularnewline
94 & 0.73590815125279 & 0.52818369749442 & 0.26409184874721 \tabularnewline
95 & 0.712486147984418 & 0.575027704031165 & 0.287513852015582 \tabularnewline
96 & 0.685269387999162 & 0.629461224001676 & 0.314730612000838 \tabularnewline
97 & 0.658653584824791 & 0.682692830350418 & 0.341346415175209 \tabularnewline
98 & 0.663888207072625 & 0.672223585854751 & 0.336111792927375 \tabularnewline
99 & 0.62948285613517 & 0.741034287729659 & 0.37051714386483 \tabularnewline
100 & 0.624845939045735 & 0.750308121908529 & 0.375154060954264 \tabularnewline
101 & 0.599118775140424 & 0.801762449719152 & 0.400881224859576 \tabularnewline
102 & 0.571692897818598 & 0.856614204362803 & 0.428307102181402 \tabularnewline
103 & 0.624790327207679 & 0.750419345584643 & 0.375209672792321 \tabularnewline
104 & 0.61154671942661 & 0.77690656114678 & 0.38845328057339 \tabularnewline
105 & 0.628312445253417 & 0.743375109493166 & 0.371687554746583 \tabularnewline
106 & 0.604361852891235 & 0.79127629421753 & 0.395638147108765 \tabularnewline
107 & 0.59704234072136 & 0.805915318557279 & 0.40295765927864 \tabularnewline
108 & 0.632336917806617 & 0.735326164386766 & 0.367663082193383 \tabularnewline
109 & 0.599043736681525 & 0.801912526636949 & 0.400956263318475 \tabularnewline
110 & 0.573060177624452 & 0.853879644751096 & 0.426939822375548 \tabularnewline
111 & 0.577627847899888 & 0.844744304200224 & 0.422372152100112 \tabularnewline
112 & 0.601679028647839 & 0.796641942704323 & 0.398320971352161 \tabularnewline
113 & 0.608303815787247 & 0.783392368425507 & 0.391696184212753 \tabularnewline
114 & 0.694164173523918 & 0.611671652952165 & 0.305835826476082 \tabularnewline
115 & 0.666276090684675 & 0.667447818630649 & 0.333723909315325 \tabularnewline
116 & 0.640087382902605 & 0.719825234194789 & 0.359912617097395 \tabularnewline
117 & 0.606410624124189 & 0.787178751751622 & 0.393589375875811 \tabularnewline
118 & 0.582057461326639 & 0.835885077346722 & 0.417942538673361 \tabularnewline
119 & 0.546548089869891 & 0.906903820260218 & 0.453451910130109 \tabularnewline
120 & 0.513609006111762 & 0.972781987776477 & 0.486390993888238 \tabularnewline
121 & 0.487943540712756 & 0.975887081425513 & 0.512056459287244 \tabularnewline
122 & 0.453202559681628 & 0.906405119363257 & 0.546797440318372 \tabularnewline
123 & 0.42467003915564 & 0.84934007831128 & 0.57532996084436 \tabularnewline
124 & 0.394463653610867 & 0.788927307221733 & 0.605536346389133 \tabularnewline
125 & 0.384930132109889 & 0.769860264219778 & 0.615069867890111 \tabularnewline
126 & 0.356969209187713 & 0.713938418375426 & 0.643030790812287 \tabularnewline
127 & 0.341924686993399 & 0.683849373986797 & 0.658075313006601 \tabularnewline
128 & 0.44541188476458 & 0.890823769529159 & 0.55458811523542 \tabularnewline
129 & 0.425659716488626 & 0.851319432977252 & 0.574340283511374 \tabularnewline
130 & 0.415001389858929 & 0.830002779717857 & 0.584998610141071 \tabularnewline
131 & 0.404128587447727 & 0.808257174895453 & 0.595871412552273 \tabularnewline
132 & 0.372375723264924 & 0.744751446529848 & 0.627624276735076 \tabularnewline
133 & 0.39214001098729 & 0.78428002197458 & 0.60785998901271 \tabularnewline
134 & 0.362381993293188 & 0.724763986586375 & 0.637618006706812 \tabularnewline
135 & 0.372619436880956 & 0.745238873761911 & 0.627380563119044 \tabularnewline
136 & 0.362071174713363 & 0.724142349426727 & 0.637928825286637 \tabularnewline
137 & 0.332623047221698 & 0.665246094443396 & 0.667376952778302 \tabularnewline
138 & 0.308965226002405 & 0.617930452004811 & 0.691034773997595 \tabularnewline
139 & 0.281664756567371 & 0.563329513134742 & 0.718335243432629 \tabularnewline
140 & 0.258193447796795 & 0.51638689559359 & 0.741806552203205 \tabularnewline
141 & 0.231052603578473 & 0.462105207156945 & 0.768947396421527 \tabularnewline
142 & 0.233722458372531 & 0.467444916745062 & 0.766277541627469 \tabularnewline
143 & 0.208861408977841 & 0.417722817955682 & 0.791138591022159 \tabularnewline
144 & 0.188999813136109 & 0.377999626272218 & 0.811000186863891 \tabularnewline
145 & 0.173811170942712 & 0.347622341885425 & 0.826188829057288 \tabularnewline
146 & 0.165792100222309 & 0.331584200444619 & 0.834207899777691 \tabularnewline
147 & 0.174747493645771 & 0.349494987291542 & 0.825252506354229 \tabularnewline
148 & 0.193197461976993 & 0.386394923953987 & 0.806802538023007 \tabularnewline
149 & 0.213052718168171 & 0.426105436336342 & 0.786947281831829 \tabularnewline
150 & 0.208342943371085 & 0.41668588674217 & 0.791657056628915 \tabularnewline
151 & 0.184880365369579 & 0.369760730739157 & 0.815119634630421 \tabularnewline
152 & 0.163935713260435 & 0.32787142652087 & 0.836064286739565 \tabularnewline
153 & 0.175711597194125 & 0.351423194388251 & 0.824288402805875 \tabularnewline
154 & 0.193034551795345 & 0.38606910359069 & 0.806965448204655 \tabularnewline
155 & 0.17704299693838 & 0.35408599387676 & 0.82295700306162 \tabularnewline
156 & 0.156516834525016 & 0.313033669050033 & 0.843483165474984 \tabularnewline
157 & 0.138162922344659 & 0.276325844689317 & 0.861837077655341 \tabularnewline
158 & 0.220713260206257 & 0.441426520412515 & 0.779286739793743 \tabularnewline
159 & 0.239222079117856 & 0.478444158235711 & 0.760777920882144 \tabularnewline
160 & 0.215684182797783 & 0.431368365595565 & 0.784315817202217 \tabularnewline
161 & 0.192529133347854 & 0.385058266695708 & 0.807470866652146 \tabularnewline
162 & 0.170378204858032 & 0.340756409716064 & 0.829621795141968 \tabularnewline
163 & 0.149573385735602 & 0.299146771471203 & 0.850426614264398 \tabularnewline
164 & 0.24922837251704 & 0.49845674503408 & 0.75077162748296 \tabularnewline
165 & 0.24462845824321 & 0.489256916486419 & 0.75537154175679 \tabularnewline
166 & 0.240873896996872 & 0.481747793993744 & 0.759126103003128 \tabularnewline
167 & 0.213109315641377 & 0.426218631282754 & 0.786890684358623 \tabularnewline
168 & 0.192344093067894 & 0.384688186135788 & 0.807655906932106 \tabularnewline
169 & 0.246770298355783 & 0.493540596711566 & 0.753229701644217 \tabularnewline
170 & 0.276300741352705 & 0.552601482705411 & 0.723699258647295 \tabularnewline
171 & 0.247181423985809 & 0.494362847971617 & 0.752818576014191 \tabularnewline
172 & 0.243205360469057 & 0.486410720938114 & 0.756794639530943 \tabularnewline
173 & 0.410689133104865 & 0.82137826620973 & 0.589310866895135 \tabularnewline
174 & 0.392220226391188 & 0.784440452782376 & 0.607779773608812 \tabularnewline
175 & 0.412770232652139 & 0.825540465304279 & 0.587229767347861 \tabularnewline
176 & 0.424673914592892 & 0.849347829185784 & 0.575326085407108 \tabularnewline
177 & 0.488241940798592 & 0.976483881597185 & 0.511758059201408 \tabularnewline
178 & 0.451351665037239 & 0.902703330074478 & 0.548648334962761 \tabularnewline
179 & 0.429106630008802 & 0.858213260017604 & 0.570893369991198 \tabularnewline
180 & 0.436634789554924 & 0.873269579109848 & 0.563365210445076 \tabularnewline
181 & 0.401252439032733 & 0.802504878065467 & 0.598747560967267 \tabularnewline
182 & 0.405301567286676 & 0.810603134573351 & 0.594698432713324 \tabularnewline
183 & 0.369248767878175 & 0.73849753575635 & 0.630751232121825 \tabularnewline
184 & 0.347222662190055 & 0.69444532438011 & 0.652777337809945 \tabularnewline
185 & 0.348009242115364 & 0.696018484230728 & 0.651990757884636 \tabularnewline
186 & 0.33325612673338 & 0.666512253466761 & 0.66674387326662 \tabularnewline
187 & 0.298864354559554 & 0.597728709119108 & 0.701135645440446 \tabularnewline
188 & 0.26771747213973 & 0.53543494427946 & 0.73228252786027 \tabularnewline
189 & 0.236845572457005 & 0.473691144914009 & 0.763154427542995 \tabularnewline
190 & 0.212169009253411 & 0.424338018506822 & 0.787830990746589 \tabularnewline
191 & 0.22496492807197 & 0.449929856143939 & 0.77503507192803 \tabularnewline
192 & 0.208077250608847 & 0.416154501217694 & 0.791922749391153 \tabularnewline
193 & 0.214436902185308 & 0.428873804370617 & 0.785563097814692 \tabularnewline
194 & 0.205426010360765 & 0.41085202072153 & 0.794573989639235 \tabularnewline
195 & 0.201953087062666 & 0.403906174125332 & 0.798046912937334 \tabularnewline
196 & 0.214687497891888 & 0.429374995783777 & 0.785312502108112 \tabularnewline
197 & 0.253498161959761 & 0.506996323919521 & 0.74650183804024 \tabularnewline
198 & 0.234682653781754 & 0.469365307563507 & 0.765317346218246 \tabularnewline
199 & 0.270765489155687 & 0.541530978311374 & 0.729234510844313 \tabularnewline
200 & 0.239015465872891 & 0.478030931745783 & 0.760984534127109 \tabularnewline
201 & 0.280027955803309 & 0.560055911606619 & 0.719972044196691 \tabularnewline
202 & 0.254653509817062 & 0.509307019634124 & 0.745346490182938 \tabularnewline
203 & 0.292124344489456 & 0.584248688978911 & 0.707875655510544 \tabularnewline
204 & 0.259969252837397 & 0.519938505674794 & 0.740030747162603 \tabularnewline
205 & 0.22745738589182 & 0.45491477178364 & 0.77254261410818 \tabularnewline
206 & 0.207397444690094 & 0.414794889380188 & 0.792602555309906 \tabularnewline
207 & 0.177436225474076 & 0.354872450948152 & 0.822563774525924 \tabularnewline
208 & 0.203027785234099 & 0.406055570468198 & 0.796972214765901 \tabularnewline
209 & 0.173296754997468 & 0.346593509994936 & 0.826703245002532 \tabularnewline
210 & 0.182879963933337 & 0.365759927866673 & 0.817120036066663 \tabularnewline
211 & 0.202371142284392 & 0.404742284568784 & 0.797628857715608 \tabularnewline
212 & 0.193138180606452 & 0.386276361212903 & 0.806861819393548 \tabularnewline
213 & 0.167702811312441 & 0.335405622624881 & 0.832297188687559 \tabularnewline
214 & 0.207497151878626 & 0.414994303757253 & 0.792502848121374 \tabularnewline
215 & 0.184428293343183 & 0.368856586686366 & 0.815571706656817 \tabularnewline
216 & 0.173170346003696 & 0.346340692007392 & 0.826829653996304 \tabularnewline
217 & 0.203513297428738 & 0.407026594857476 & 0.796486702571262 \tabularnewline
218 & 0.175634909014549 & 0.351269818029099 & 0.824365090985451 \tabularnewline
219 & 0.162208946939911 & 0.324417893879821 & 0.837791053060089 \tabularnewline
220 & 0.179585510343434 & 0.359171020686868 & 0.820414489656566 \tabularnewline
221 & 0.184036278011884 & 0.368072556023768 & 0.815963721988116 \tabularnewline
222 & 0.181746706548737 & 0.363493413097473 & 0.818253293451263 \tabularnewline
223 & 0.165197396929427 & 0.330394793858855 & 0.834802603070573 \tabularnewline
224 & 0.137122343527303 & 0.274244687054606 & 0.862877656472697 \tabularnewline
225 & 0.134548360790914 & 0.269096721581828 & 0.865451639209086 \tabularnewline
226 & 0.165346628801671 & 0.330693257603343 & 0.834653371198329 \tabularnewline
227 & 0.393669898915416 & 0.787339797830833 & 0.606330101084584 \tabularnewline
228 & 0.352069730881051 & 0.704139461762102 & 0.647930269118949 \tabularnewline
229 & 0.313150719867205 & 0.62630143973441 & 0.686849280132795 \tabularnewline
230 & 0.287283991181725 & 0.57456798236345 & 0.712716008818275 \tabularnewline
231 & 0.251073152870738 & 0.502146305741476 & 0.748926847129262 \tabularnewline
232 & 0.280320448592053 & 0.560640897184106 & 0.719679551407947 \tabularnewline
233 & 0.23743779856189 & 0.474875597123779 & 0.76256220143811 \tabularnewline
234 & 0.199221224437412 & 0.398442448874823 & 0.800778775562588 \tabularnewline
235 & 0.206315170431845 & 0.412630340863691 & 0.793684829568155 \tabularnewline
236 & 0.178083516839939 & 0.356167033679878 & 0.821916483160061 \tabularnewline
237 & 0.153705961347879 & 0.307411922695758 & 0.846294038652121 \tabularnewline
238 & 0.118455954200374 & 0.236911908400749 & 0.881544045799626 \tabularnewline
239 & 0.227057213068516 & 0.454114426137033 & 0.772942786931484 \tabularnewline
240 & 0.237028926785239 & 0.474057853570478 & 0.762971073214761 \tabularnewline
241 & 0.240691772375789 & 0.481383544751579 & 0.759308227624211 \tabularnewline
242 & 0.858485866888979 & 0.283028266222041 & 0.141514133111021 \tabularnewline
243 & 0.808802612887242 & 0.382394774225516 & 0.191197387112758 \tabularnewline
244 & 0.75498620431642 & 0.490027591367159 & 0.24501379568358 \tabularnewline
245 & 0.701437164652613 & 0.597125670694773 & 0.298562835347387 \tabularnewline
246 & 0.632539371239122 & 0.734921257521757 & 0.367460628760878 \tabularnewline
247 & 0.646816538359354 & 0.706366923281292 & 0.353183461640646 \tabularnewline
248 & 0.650390206329963 & 0.699219587340073 & 0.349609793670037 \tabularnewline
249 & 0.544941117302032 & 0.910117765395936 & 0.455058882697968 \tabularnewline
250 & 0.459564112673048 & 0.919128225346095 & 0.540435887326952 \tabularnewline
251 & 0.399938867832099 & 0.799877735664199 & 0.600061132167901 \tabularnewline
252 & 0.905594637001915 & 0.18881072599617 & 0.0944053629980852 \tabularnewline
253 & 0.817587062067215 & 0.364825875865569 & 0.182412937932785 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.0425068438623072[/C][C]0.0850136877246144[/C][C]0.957493156137693[/C][/ROW]
[ROW][C]12[/C][C]0.794700838239056[/C][C]0.410598323521887[/C][C]0.205299161760944[/C][/ROW]
[ROW][C]13[/C][C]0.98241287486243[/C][C]0.0351742502751407[/C][C]0.0175871251375704[/C][/ROW]
[ROW][C]14[/C][C]0.980720282239743[/C][C]0.0385594355205132[/C][C]0.0192797177602566[/C][/ROW]
[ROW][C]15[/C][C]0.981194975932255[/C][C]0.0376100481354891[/C][C]0.0188050240677445[/C][/ROW]
[ROW][C]16[/C][C]0.969725205941906[/C][C]0.0605495881161878[/C][C]0.0302747940580939[/C][/ROW]
[ROW][C]17[/C][C]0.965549382967397[/C][C]0.0689012340652062[/C][C]0.0344506170326031[/C][/ROW]
[ROW][C]18[/C][C]0.949247439560944[/C][C]0.101505120878112[/C][C]0.0507525604390562[/C][/ROW]
[ROW][C]19[/C][C]0.933755862106714[/C][C]0.132488275786573[/C][C]0.0662441378932863[/C][/ROW]
[ROW][C]20[/C][C]0.918358335881688[/C][C]0.163283328236625[/C][C]0.0816416641183124[/C][/ROW]
[ROW][C]21[/C][C]0.915898991027098[/C][C]0.168202017945804[/C][C]0.0841010089729021[/C][/ROW]
[ROW][C]22[/C][C]0.964821121275956[/C][C]0.0703577574480883[/C][C]0.0351788787240442[/C][/ROW]
[ROW][C]23[/C][C]0.950505433444142[/C][C]0.0989891331117168[/C][C]0.0494945665558584[/C][/ROW]
[ROW][C]24[/C][C]0.938886482062994[/C][C]0.122227035874012[/C][C]0.0611135179370062[/C][/ROW]
[ROW][C]25[/C][C]0.918804960910381[/C][C]0.162390078179238[/C][C]0.081195039089619[/C][/ROW]
[ROW][C]26[/C][C]0.99850601721563[/C][C]0.00298796556873916[/C][C]0.00149398278436958[/C][/ROW]
[ROW][C]27[/C][C]0.997945414708241[/C][C]0.00410917058351759[/C][C]0.00205458529175879[/C][/ROW]
[ROW][C]28[/C][C]0.996818269599194[/C][C]0.0063634608016122[/C][C]0.0031817304008061[/C][/ROW]
[ROW][C]29[/C][C]0.995353144488057[/C][C]0.00929371102388503[/C][C]0.00464685551194252[/C][/ROW]
[ROW][C]30[/C][C]0.996302329921236[/C][C]0.00739534015752766[/C][C]0.00369767007876383[/C][/ROW]
[ROW][C]31[/C][C]0.994776653900234[/C][C]0.0104466921995314[/C][C]0.00522334609976569[/C][/ROW]
[ROW][C]32[/C][C]0.99228876439031[/C][C]0.0154224712193805[/C][C]0.00771123560969024[/C][/ROW]
[ROW][C]33[/C][C]0.99219219601825[/C][C]0.0156156079634994[/C][C]0.00780780398174971[/C][/ROW]
[ROW][C]34[/C][C]0.988758165308029[/C][C]0.0224836693839418[/C][C]0.0112418346919709[/C][/ROW]
[ROW][C]35[/C][C]0.984144581385911[/C][C]0.031710837228179[/C][C]0.0158554186140895[/C][/ROW]
[ROW][C]36[/C][C]0.978335816473567[/C][C]0.0433283670528667[/C][C]0.0216641835264333[/C][/ROW]
[ROW][C]37[/C][C]0.980600913640328[/C][C]0.0387981727193449[/C][C]0.0193990863596724[/C][/ROW]
[ROW][C]38[/C][C]0.975116231421354[/C][C]0.0497675371572929[/C][C]0.0248837685786465[/C][/ROW]
[ROW][C]39[/C][C]0.974707073145074[/C][C]0.050585853709853[/C][C]0.0252929268549265[/C][/ROW]
[ROW][C]40[/C][C]0.97136754905843[/C][C]0.05726490188314[/C][C]0.02863245094157[/C][/ROW]
[ROW][C]41[/C][C]0.962227409678992[/C][C]0.0755451806420159[/C][C]0.037772590321008[/C][/ROW]
[ROW][C]42[/C][C]0.964962482495153[/C][C]0.0700750350096942[/C][C]0.0350375175048471[/C][/ROW]
[ROW][C]43[/C][C]0.957005843058951[/C][C]0.0859883138820978[/C][C]0.0429941569410489[/C][/ROW]
[ROW][C]44[/C][C]0.946281487452687[/C][C]0.107437025094627[/C][C]0.0537185125473134[/C][/ROW]
[ROW][C]45[/C][C]0.932158020237723[/C][C]0.135683959524554[/C][C]0.0678419797622771[/C][/ROW]
[ROW][C]46[/C][C]0.934589548354426[/C][C]0.130820903291149[/C][C]0.0654104516455744[/C][/ROW]
[ROW][C]47[/C][C]0.921108994555532[/C][C]0.157782010888935[/C][C]0.0788910054444675[/C][/ROW]
[ROW][C]48[/C][C]0.904111631395358[/C][C]0.191776737209284[/C][C]0.0958883686046418[/C][/ROW]
[ROW][C]49[/C][C]0.938706300897907[/C][C]0.122587398204187[/C][C]0.0612936991020933[/C][/ROW]
[ROW][C]50[/C][C]0.931636975090992[/C][C]0.136726049818017[/C][C]0.0683630249090084[/C][/ROW]
[ROW][C]51[/C][C]0.919802995620511[/C][C]0.160394008758978[/C][C]0.0801970043794888[/C][/ROW]
[ROW][C]52[/C][C]0.902915219708319[/C][C]0.194169560583362[/C][C]0.097084780291681[/C][/ROW]
[ROW][C]53[/C][C]0.884500128631802[/C][C]0.230999742736396[/C][C]0.115499871368198[/C][/ROW]
[ROW][C]54[/C][C]0.869656935490374[/C][C]0.260686129019252[/C][C]0.130343064509626[/C][/ROW]
[ROW][C]55[/C][C]0.858706620196001[/C][C]0.282586759607997[/C][C]0.141293379803999[/C][/ROW]
[ROW][C]56[/C][C]0.844248980147128[/C][C]0.311502039705744[/C][C]0.155751019852872[/C][/ROW]
[ROW][C]57[/C][C]0.83881568284603[/C][C]0.322368634307941[/C][C]0.16118431715397[/C][/ROW]
[ROW][C]58[/C][C]0.811012829447651[/C][C]0.377974341104698[/C][C]0.188987170552349[/C][/ROW]
[ROW][C]59[/C][C]0.835451391449088[/C][C]0.329097217101823[/C][C]0.164548608550912[/C][/ROW]
[ROW][C]60[/C][C]0.818941763869628[/C][C]0.362116472260744[/C][C]0.181058236130372[/C][/ROW]
[ROW][C]61[/C][C]0.830278126722401[/C][C]0.339443746555198[/C][C]0.169721873277599[/C][/ROW]
[ROW][C]62[/C][C]0.81295620751139[/C][C]0.374087584977221[/C][C]0.18704379248861[/C][/ROW]
[ROW][C]63[/C][C]0.850559577155151[/C][C]0.298880845689697[/C][C]0.149440422844849[/C][/ROW]
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[ROW][C]204[/C][C]0.259969252837397[/C][C]0.519938505674794[/C][C]0.740030747162603[/C][/ROW]
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[ROW][C]208[/C][C]0.203027785234099[/C][C]0.406055570468198[/C][C]0.796972214765901[/C][/ROW]
[ROW][C]209[/C][C]0.173296754997468[/C][C]0.346593509994936[/C][C]0.826703245002532[/C][/ROW]
[ROW][C]210[/C][C]0.182879963933337[/C][C]0.365759927866673[/C][C]0.817120036066663[/C][/ROW]
[ROW][C]211[/C][C]0.202371142284392[/C][C]0.404742284568784[/C][C]0.797628857715608[/C][/ROW]
[ROW][C]212[/C][C]0.193138180606452[/C][C]0.386276361212903[/C][C]0.806861819393548[/C][/ROW]
[ROW][C]213[/C][C]0.167702811312441[/C][C]0.335405622624881[/C][C]0.832297188687559[/C][/ROW]
[ROW][C]214[/C][C]0.207497151878626[/C][C]0.414994303757253[/C][C]0.792502848121374[/C][/ROW]
[ROW][C]215[/C][C]0.184428293343183[/C][C]0.368856586686366[/C][C]0.815571706656817[/C][/ROW]
[ROW][C]216[/C][C]0.173170346003696[/C][C]0.346340692007392[/C][C]0.826829653996304[/C][/ROW]
[ROW][C]217[/C][C]0.203513297428738[/C][C]0.407026594857476[/C][C]0.796486702571262[/C][/ROW]
[ROW][C]218[/C][C]0.175634909014549[/C][C]0.351269818029099[/C][C]0.824365090985451[/C][/ROW]
[ROW][C]219[/C][C]0.162208946939911[/C][C]0.324417893879821[/C][C]0.837791053060089[/C][/ROW]
[ROW][C]220[/C][C]0.179585510343434[/C][C]0.359171020686868[/C][C]0.820414489656566[/C][/ROW]
[ROW][C]221[/C][C]0.184036278011884[/C][C]0.368072556023768[/C][C]0.815963721988116[/C][/ROW]
[ROW][C]222[/C][C]0.181746706548737[/C][C]0.363493413097473[/C][C]0.818253293451263[/C][/ROW]
[ROW][C]223[/C][C]0.165197396929427[/C][C]0.330394793858855[/C][C]0.834802603070573[/C][/ROW]
[ROW][C]224[/C][C]0.137122343527303[/C][C]0.274244687054606[/C][C]0.862877656472697[/C][/ROW]
[ROW][C]225[/C][C]0.134548360790914[/C][C]0.269096721581828[/C][C]0.865451639209086[/C][/ROW]
[ROW][C]226[/C][C]0.165346628801671[/C][C]0.330693257603343[/C][C]0.834653371198329[/C][/ROW]
[ROW][C]227[/C][C]0.393669898915416[/C][C]0.787339797830833[/C][C]0.606330101084584[/C][/ROW]
[ROW][C]228[/C][C]0.352069730881051[/C][C]0.704139461762102[/C][C]0.647930269118949[/C][/ROW]
[ROW][C]229[/C][C]0.313150719867205[/C][C]0.62630143973441[/C][C]0.686849280132795[/C][/ROW]
[ROW][C]230[/C][C]0.287283991181725[/C][C]0.57456798236345[/C][C]0.712716008818275[/C][/ROW]
[ROW][C]231[/C][C]0.251073152870738[/C][C]0.502146305741476[/C][C]0.748926847129262[/C][/ROW]
[ROW][C]232[/C][C]0.280320448592053[/C][C]0.560640897184106[/C][C]0.719679551407947[/C][/ROW]
[ROW][C]233[/C][C]0.23743779856189[/C][C]0.474875597123779[/C][C]0.76256220143811[/C][/ROW]
[ROW][C]234[/C][C]0.199221224437412[/C][C]0.398442448874823[/C][C]0.800778775562588[/C][/ROW]
[ROW][C]235[/C][C]0.206315170431845[/C][C]0.412630340863691[/C][C]0.793684829568155[/C][/ROW]
[ROW][C]236[/C][C]0.178083516839939[/C][C]0.356167033679878[/C][C]0.821916483160061[/C][/ROW]
[ROW][C]237[/C][C]0.153705961347879[/C][C]0.307411922695758[/C][C]0.846294038652121[/C][/ROW]
[ROW][C]238[/C][C]0.118455954200374[/C][C]0.236911908400749[/C][C]0.881544045799626[/C][/ROW]
[ROW][C]239[/C][C]0.227057213068516[/C][C]0.454114426137033[/C][C]0.772942786931484[/C][/ROW]
[ROW][C]240[/C][C]0.237028926785239[/C][C]0.474057853570478[/C][C]0.762971073214761[/C][/ROW]
[ROW][C]241[/C][C]0.240691772375789[/C][C]0.481383544751579[/C][C]0.759308227624211[/C][/ROW]
[ROW][C]242[/C][C]0.858485866888979[/C][C]0.283028266222041[/C][C]0.141514133111021[/C][/ROW]
[ROW][C]243[/C][C]0.808802612887242[/C][C]0.382394774225516[/C][C]0.191197387112758[/C][/ROW]
[ROW][C]244[/C][C]0.75498620431642[/C][C]0.490027591367159[/C][C]0.24501379568358[/C][/ROW]
[ROW][C]245[/C][C]0.701437164652613[/C][C]0.597125670694773[/C][C]0.298562835347387[/C][/ROW]
[ROW][C]246[/C][C]0.632539371239122[/C][C]0.734921257521757[/C][C]0.367460628760878[/C][/ROW]
[ROW][C]247[/C][C]0.646816538359354[/C][C]0.706366923281292[/C][C]0.353183461640646[/C][/ROW]
[ROW][C]248[/C][C]0.650390206329963[/C][C]0.699219587340073[/C][C]0.349609793670037[/C][/ROW]
[ROW][C]249[/C][C]0.544941117302032[/C][C]0.910117765395936[/C][C]0.455058882697968[/C][/ROW]
[ROW][C]250[/C][C]0.459564112673048[/C][C]0.919128225346095[/C][C]0.540435887326952[/C][/ROW]
[ROW][C]251[/C][C]0.399938867832099[/C][C]0.799877735664199[/C][C]0.600061132167901[/C][/ROW]
[ROW][C]252[/C][C]0.905594637001915[/C][C]0.18881072599617[/C][C]0.0944053629980852[/C][/ROW]
[ROW][C]253[/C][C]0.817587062067215[/C][C]0.364825875865569[/C][C]0.182412937932785[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04250684386230720.08501368772461440.957493156137693
120.7947008382390560.4105983235218870.205299161760944
130.982412874862430.03517425027514070.0175871251375704
140.9807202822397430.03855943552051320.0192797177602566
150.9811949759322550.03761004813548910.0188050240677445
160.9697252059419060.06054958811618780.0302747940580939
170.9655493829673970.06890123406520620.0344506170326031
180.9492474395609440.1015051208781120.0507525604390562
190.9337558621067140.1324882757865730.0662441378932863
200.9183583358816880.1632833282366250.0816416641183124
210.9158989910270980.1682020179458040.0841010089729021
220.9648211212759560.07035775744808830.0351788787240442
230.9505054334441420.09898913311171680.0494945665558584
240.9388864820629940.1222270358740120.0611135179370062
250.9188049609103810.1623900781792380.081195039089619
260.998506017215630.002987965568739160.00149398278436958
270.9979454147082410.004109170583517590.00205458529175879
280.9968182695991940.00636346080161220.0031817304008061
290.9953531444880570.009293711023885030.00464685551194252
300.9963023299212360.007395340157527660.00369767007876383
310.9947766539002340.01044669219953140.00522334609976569
320.992288764390310.01542247121938050.00771123560969024
330.992192196018250.01561560796349940.00780780398174971
340.9887581653080290.02248366938394180.0112418346919709
350.9841445813859110.0317108372281790.0158554186140895
360.9783358164735670.04332836705286670.0216641835264333
370.9806009136403280.03879817271934490.0193990863596724
380.9751162314213540.04976753715729290.0248837685786465
390.9747070731450740.0505858537098530.0252929268549265
400.971367549058430.057264901883140.02863245094157
410.9622274096789920.07554518064201590.037772590321008
420.9649624824951530.07007503500969420.0350375175048471
430.9570058430589510.08598831388209780.0429941569410489
440.9462814874526870.1074370250946270.0537185125473134
450.9321580202377230.1356839595245540.0678419797622771
460.9345895483544260.1308209032911490.0654104516455744
470.9211089945555320.1577820108889350.0788910054444675
480.9041116313953580.1917767372092840.0958883686046418
490.9387063008979070.1225873982041870.0612936991020933
500.9316369750909920.1367260498180170.0683630249090084
510.9198029956205110.1603940087589780.0801970043794888
520.9029152197083190.1941695605833620.097084780291681
530.8845001286318020.2309997427363960.115499871368198
540.8696569354903740.2606861290192520.130343064509626
550.8587066201960010.2825867596079970.141293379803999
560.8442489801471280.3115020397057440.155751019852872
570.838815682846030.3223686343079410.16118431715397
580.8110128294476510.3779743411046980.188987170552349
590.8354513914490880.3290972171018230.164548608550912
600.8189417638696280.3621164722607440.181058236130372
610.8302781267224010.3394437465551980.169721873277599
620.812956207511390.3740875849772210.18704379248861
630.8505595771551510.2988808456896970.149440422844849
640.8477362974496540.3045274051006920.152263702550346
650.8498130891981980.3003738216036040.150186910801802
660.9228470068378940.1543059863242120.0771529931621059
670.9118797598522520.1762404802954950.0881202401477475
680.9117773962569510.1764452074860980.088222603743049
690.9151409888320310.1697180223359390.0848590111679693
700.9150834712575110.1698330574849790.0849165287424895
710.9022530626517470.1954938746965060.097746937348253
720.9332176620899110.1335646758201770.0667823379100886
730.9227029464356950.1545941071286110.0772970535643055
740.9077743539920480.1844512920159040.0922256460079518
750.8990966553550270.2018066892899450.100903344644973
760.8816020106499020.2367959787001960.118397989350098
770.9097707023082430.1804585953835150.0902292976917573
780.8959308757266510.2081382485466980.104069124273349
790.8899835669833640.2200328660332720.110016433016636
800.8820128996635510.2359742006728980.117987100336449
810.8630669609478560.2738660781042890.136933039052144
820.8421889505595420.3156220988809150.157811049440458
830.8423175447802820.3153649104394350.157682455219718
840.8241002381847320.3517995236305360.175899761815268
850.7998811446838030.4002377106323930.200118855316197
860.7764911827949220.4470176344101570.223508817205078
870.7480016770706710.5039966458586590.251998322929329
880.7189714502093570.5620570995812860.281028549790643
890.7872332721899490.4255334556201010.212766727810051
900.8255917846655550.348816430668890.174408215334445
910.8040579595726840.3918840808546320.195942040427316
920.779483683672240.441032632655520.22051631632776
930.7595222874856250.4809554250287510.240477712514375
940.735908151252790.528183697494420.26409184874721
950.7124861479844180.5750277040311650.287513852015582
960.6852693879991620.6294612240016760.314730612000838
970.6586535848247910.6826928303504180.341346415175209
980.6638882070726250.6722235858547510.336111792927375
990.629482856135170.7410342877296590.37051714386483
1000.6248459390457350.7503081219085290.375154060954264
1010.5991187751404240.8017624497191520.400881224859576
1020.5716928978185980.8566142043628030.428307102181402
1030.6247903272076790.7504193455846430.375209672792321
1040.611546719426610.776906561146780.38845328057339
1050.6283124452534170.7433751094931660.371687554746583
1060.6043618528912350.791276294217530.395638147108765
1070.597042340721360.8059153185572790.40295765927864
1080.6323369178066170.7353261643867660.367663082193383
1090.5990437366815250.8019125266369490.400956263318475
1100.5730601776244520.8538796447510960.426939822375548
1110.5776278478998880.8447443042002240.422372152100112
1120.6016790286478390.7966419427043230.398320971352161
1130.6083038157872470.7833923684255070.391696184212753
1140.6941641735239180.6116716529521650.305835826476082
1150.6662760906846750.6674478186306490.333723909315325
1160.6400873829026050.7198252341947890.359912617097395
1170.6064106241241890.7871787517516220.393589375875811
1180.5820574613266390.8358850773467220.417942538673361
1190.5465480898698910.9069038202602180.453451910130109
1200.5136090061117620.9727819877764770.486390993888238
1210.4879435407127560.9758870814255130.512056459287244
1220.4532025596816280.9064051193632570.546797440318372
1230.424670039155640.849340078311280.57532996084436
1240.3944636536108670.7889273072217330.605536346389133
1250.3849301321098890.7698602642197780.615069867890111
1260.3569692091877130.7139384183754260.643030790812287
1270.3419246869933990.6838493739867970.658075313006601
1280.445411884764580.8908237695291590.55458811523542
1290.4256597164886260.8513194329772520.574340283511374
1300.4150013898589290.8300027797178570.584998610141071
1310.4041285874477270.8082571748954530.595871412552273
1320.3723757232649240.7447514465298480.627624276735076
1330.392140010987290.784280021974580.60785998901271
1340.3623819932931880.7247639865863750.637618006706812
1350.3726194368809560.7452388737619110.627380563119044
1360.3620711747133630.7241423494267270.637928825286637
1370.3326230472216980.6652460944433960.667376952778302
1380.3089652260024050.6179304520048110.691034773997595
1390.2816647565673710.5633295131347420.718335243432629
1400.2581934477967950.516386895593590.741806552203205
1410.2310526035784730.4621052071569450.768947396421527
1420.2337224583725310.4674449167450620.766277541627469
1430.2088614089778410.4177228179556820.791138591022159
1440.1889998131361090.3779996262722180.811000186863891
1450.1738111709427120.3476223418854250.826188829057288
1460.1657921002223090.3315842004446190.834207899777691
1470.1747474936457710.3494949872915420.825252506354229
1480.1931974619769930.3863949239539870.806802538023007
1490.2130527181681710.4261054363363420.786947281831829
1500.2083429433710850.416685886742170.791657056628915
1510.1848803653695790.3697607307391570.815119634630421
1520.1639357132604350.327871426520870.836064286739565
1530.1757115971941250.3514231943882510.824288402805875
1540.1930345517953450.386069103590690.806965448204655
1550.177042996938380.354085993876760.82295700306162
1560.1565168345250160.3130336690500330.843483165474984
1570.1381629223446590.2763258446893170.861837077655341
1580.2207132602062570.4414265204125150.779286739793743
1590.2392220791178560.4784441582357110.760777920882144
1600.2156841827977830.4313683655955650.784315817202217
1610.1925291333478540.3850582666957080.807470866652146
1620.1703782048580320.3407564097160640.829621795141968
1630.1495733857356020.2991467714712030.850426614264398
1640.249228372517040.498456745034080.75077162748296
1650.244628458243210.4892569164864190.75537154175679
1660.2408738969968720.4817477939937440.759126103003128
1670.2131093156413770.4262186312827540.786890684358623
1680.1923440930678940.3846881861357880.807655906932106
1690.2467702983557830.4935405967115660.753229701644217
1700.2763007413527050.5526014827054110.723699258647295
1710.2471814239858090.4943628479716170.752818576014191
1720.2432053604690570.4864107209381140.756794639530943
1730.4106891331048650.821378266209730.589310866895135
1740.3922202263911880.7844404527823760.607779773608812
1750.4127702326521390.8255404653042790.587229767347861
1760.4246739145928920.8493478291857840.575326085407108
1770.4882419407985920.9764838815971850.511758059201408
1780.4513516650372390.9027033300744780.548648334962761
1790.4291066300088020.8582132600176040.570893369991198
1800.4366347895549240.8732695791098480.563365210445076
1810.4012524390327330.8025048780654670.598747560967267
1820.4053015672866760.8106031345733510.594698432713324
1830.3692487678781750.738497535756350.630751232121825
1840.3472226621900550.694445324380110.652777337809945
1850.3480092421153640.6960184842307280.651990757884636
1860.333256126733380.6665122534667610.66674387326662
1870.2988643545595540.5977287091191080.701135645440446
1880.267717472139730.535434944279460.73228252786027
1890.2368455724570050.4736911449140090.763154427542995
1900.2121690092534110.4243380185068220.787830990746589
1910.224964928071970.4499298561439390.77503507192803
1920.2080772506088470.4161545012176940.791922749391153
1930.2144369021853080.4288738043706170.785563097814692
1940.2054260103607650.410852020721530.794573989639235
1950.2019530870626660.4039061741253320.798046912937334
1960.2146874978918880.4293749957837770.785312502108112
1970.2534981619597610.5069963239195210.74650183804024
1980.2346826537817540.4693653075635070.765317346218246
1990.2707654891556870.5415309783113740.729234510844313
2000.2390154658728910.4780309317457830.760984534127109
2010.2800279558033090.5600559116066190.719972044196691
2020.2546535098170620.5093070196341240.745346490182938
2030.2921243444894560.5842486889789110.707875655510544
2040.2599692528373970.5199385056747940.740030747162603
2050.227457385891820.454914771783640.77254261410818
2060.2073974446900940.4147948893801880.792602555309906
2070.1774362254740760.3548724509481520.822563774525924
2080.2030277852340990.4060555704681980.796972214765901
2090.1732967549974680.3465935099949360.826703245002532
2100.1828799639333370.3657599278666730.817120036066663
2110.2023711422843920.4047422845687840.797628857715608
2120.1931381806064520.3862763612129030.806861819393548
2130.1677028113124410.3354056226248810.832297188687559
2140.2074971518786260.4149943037572530.792502848121374
2150.1844282933431830.3688565866863660.815571706656817
2160.1731703460036960.3463406920073920.826829653996304
2170.2035132974287380.4070265948574760.796486702571262
2180.1756349090145490.3512698180290990.824365090985451
2190.1622089469399110.3244178938798210.837791053060089
2200.1795855103434340.3591710206868680.820414489656566
2210.1840362780118840.3680725560237680.815963721988116
2220.1817467065487370.3634934130974730.818253293451263
2230.1651973969294270.3303947938588550.834802603070573
2240.1371223435273030.2742446870546060.862877656472697
2250.1345483607909140.2690967215818280.865451639209086
2260.1653466288016710.3306932576033430.834653371198329
2270.3936698989154160.7873397978308330.606330101084584
2280.3520697308810510.7041394617621020.647930269118949
2290.3131507198672050.626301439734410.686849280132795
2300.2872839911817250.574567982363450.712716008818275
2310.2510731528707380.5021463057414760.748926847129262
2320.2803204485920530.5606408971841060.719679551407947
2330.237437798561890.4748755971237790.76256220143811
2340.1992212244374120.3984424488748230.800778775562588
2350.2063151704318450.4126303408636910.793684829568155
2360.1780835168399390.3561670336798780.821916483160061
2370.1537059613478790.3074119226957580.846294038652121
2380.1184559542003740.2369119084007490.881544045799626
2390.2270572130685160.4541144261370330.772942786931484
2400.2370289267852390.4740578535704780.762971073214761
2410.2406917723757890.4813835447515790.759308227624211
2420.8584858668889790.2830282662220410.141514133111021
2430.8088026128872420.3823947742255160.191197387112758
2440.754986204316420.4900275913671590.24501379568358
2450.7014371646526130.5971256706947730.298562835347387
2460.6325393712391220.7349212575217570.367460628760878
2470.6468165383593540.7063669232812920.353183461640646
2480.6503902063299630.6992195873400730.349609793670037
2490.5449411173020320.9101177653959360.455058882697968
2500.4595641126730480.9191282253460950.540435887326952
2510.3999388678320990.7998777356641990.600061132167901
2520.9055946370019150.188810725996170.0944053629980852
2530.8175870620672150.3648258758655690.182412937932785






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0205761316872428NOK
5% type I error level160.065843621399177NOK
10% type I error level260.106995884773663NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 5 & 0.0205761316872428 & NOK \tabularnewline
5% type I error level & 16 & 0.065843621399177 & NOK \tabularnewline
10% type I error level & 26 & 0.106995884773663 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225693&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]5[/C][C]0.0205761316872428[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.065843621399177[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.106995884773663[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225693&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225693&T=6

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The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0205761316872428NOK
5% type I error level160.065843621399177NOK
10% type I error level260.106995884773663NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}